1
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Dzianok P, Kublik E. PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia. Sci Data 2024; 11:276. [PMID: 38453963 PMCID: PMC10920678 DOI: 10.1038/s41597-024-03106-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/29/2024] [Indexed: 03/09/2024] Open
Abstract
Interdisciplinary approaches are needed to understand the relationship between genetic factors and brain structure and function. Here we describe a database that includes genetic data on apolipoprotein E (APOE) and phosphatidylinositol binding clathrin assembly protein (PICALM) genes, both of which are known to increase the risk of late-onset Alzheimer's disease, paired with psychometric (memory, intelligence, mood, personality, stress coping strategies), basic demographic and health data on a cohort of 192 healthy middle-aged (50-63) individuals. Part of the database (~79 participants) also includes blood tests (blood counts, lipid profile, HSV virus) and functional neuroimaging data (EEG/fMRI) recorded with a resting-state protocol (eyes open and eyes closed) and two cognitive tasks (multi-source interference task, MSIT; and Sternberg's memory task). The data were validated and showed overall good quality. This open-science dataset is well suited not only for research relating to susceptibility to Alzheimer's disease but also for more general questions on brain aging or can be used as part of meta-analytical multi-disciplinary projects.
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Affiliation(s)
- Patrycja Dzianok
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, Warsaw, Poland
| | - Ewa Kublik
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, Warsaw, Poland.
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2
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Fiorito AM, Aleman A, Blasi G, Bourque J, Cao H, Chan RCK, Chowdury A, Conrod P, Diwadkar VA, Goghari VM, Guinjoan S, Gur RE, Gur RC, Kwon JS, Lieslehto J, Lukow PB, Meyer-Lindenberg A, Modinos G, Quarto T, Spilka MJ, Shivakumar V, Venkatasubramanian G, Villarreal M, Wang Y, Wolf DH, Yun JY, Fakra E, Sescousse G. Are Brain Responses to Emotion a Reliable Endophenotype of Schizophrenia? An Image-Based Functional Magnetic Resonance Imaging Meta-analysis. Biol Psychiatry 2023; 93:167-177. [PMID: 36085080 DOI: 10.1016/j.biopsych.2022.06.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND Impaired emotion processing constitutes a key dimension of schizophrenia and a possible endophenotype of this illness. Empirical studies consistently report poorer emotion recognition performance in patients with schizophrenia as well as in individuals at enhanced risk of schizophrenia. Functional magnetic resonance imaging studies also report consistent patterns of abnormal brain activation in response to emotional stimuli in patients, in particular, decreased amygdala activation. In contrast, brain-level abnormalities in at-risk individuals are more elusive. We address this gap using an image-based meta-analysis of the functional magnetic resonance imaging literature. METHODS Functional magnetic resonance imaging studies investigating brain responses to negative emotional stimuli and reporting a comparison between at-risk individuals and healthy control subjects were identified. Frequentist and Bayesian voxelwise meta-analyses were performed separately, by implementing a random-effect model with unthresholded group-level T-maps from individual studies as input. RESULTS In total, 17 studies with a cumulative total of 677 at-risk individuals and 805 healthy control subjects were included. Frequentist analyses did not reveal significant differences between at-risk individuals and healthy control subjects. Similar results were observed with Bayesian analyses, which provided strong evidence for the absence of meaningful brain activation differences across the entire brain. Region of interest analyses specifically focusing on the amygdala confirmed the lack of group differences in this region. CONCLUSIONS These results suggest that brain activation patterns in response to emotional stimuli are unlikely to constitute a reliable endophenotype of schizophrenia. We suggest that future studies instead focus on impaired functional connectivity as an alternative and promising endophenotype.
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Affiliation(s)
- Anna M Fiorito
- Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, PSYR2 Team, University of Lyon, Lyon, France; Department of Psychiatry, University Hospital of Saint-Etienne, Saint-Etienne, France.
| | - André Aleman
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Groningen, The Netherlands
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Josiane Bourque
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Asadur Chowdury
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, Michigan
| | - Patricia Conrod
- CHU Sainte-Justine Research Center, Department of Psychiatry and Addiction, University of Montréal, Montreal, Quebec, Canada
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, Michigan
| | - Vina M Goghari
- Department of Psychological Clinical Science, University of Toronto, Toronto, Ontario, Canada
| | | | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Johannes Lieslehto
- University of Eastern Finland, Department of Forensic Psychiatry, Niuvanniemi Hospital, Kuopio, Finland
| | - Paulina B Lukow
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Andreas Meyer-Lindenberg
- Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | | | - Michael J Spilka
- Department of Psychology, University of Georgia, Athens, Georgia
| | - Venkataram Shivakumar
- Department of Integrative Medicine, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | | | - Mirta Villarreal
- Instituto de Neurociencias FLENI-CONICET, Facultad de Ciencias Exactas y Naturales, UBA, Buenos Aires, Argentina
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Eric Fakra
- Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, PSYR2 Team, University of Lyon, Lyon, France; Department of Psychiatry, University Hospital of Saint-Etienne, Saint-Etienne, France
| | - Guillaume Sescousse
- Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, PSYR2 Team, University of Lyon, Lyon, France; Centre Hospitalier Le Vinatier, Bron, France
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3
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Qi R, Cao Z, Surento W, Zhang L, Qiu L, Xia Z, Ching CRK, Xu Q, Yin Y, Zhang LJ, Li L, Luo Y, Lu GM. RORA rs8042149 polymorphism moderates the association between PTSD symptom severity and transverse temporal gyrus thickness in Han Chinese adults who lost their only child. J Affect Disord 2022; 314:318-324. [PMID: 35878841 DOI: 10.1016/j.jad.2022.07.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/13/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND The G allele in retinoid-related orphan receptor alpha (RORA, rs8042149) gene is associated with post-traumatic stress disorder (PTSD) diagnosis and more severe symptoms, reported in the first genome-wide association study of PTSD and subsequent replication studies. Although recent MRI studies identified brain structural deficits in RORA rs8042149 risk G allele carriers, the neural mechanism underlying RORA-related brain structural changes in PTSD remains poorly understood. METHODS This study included 227 Han Chinese adults who lost their only child. Cortical thickness and subcortical volume were extracted using FreeSurfer, and PTSD severity was assessed using the Clinician-Administered PTSD Scale. Hierarchical linear regression was used to assess the interaction effect between RORA genotypes (T/T, G/T, and G/G) and PTSD severity on cortical and subcortical structures. RESULTS Significant genotype × PTSD symptom severity interaction effects were found for bilateral transverse temporal gyrus thickness. For individuals with the homozygous T/T genotype, current PTSD symptom severity was positively associated with bilateral transverse temporal gyrus thickness. For individuals with heterozygous G/T genotype, current PTSD symptom severity was negatively associated with the left transverse temporal gyrus thickness. No significant main or interaction effects were found in any subcortical regions. LIMITATION Cross-sectional design of this study. CONCLUSION These findings suggest that the non-risk T/T genotype - but not the risk G allele carriers - has a potentially protective or compensatory role on temporal gyrus thickness in adults who lost their only child. These results highlight the moderation effect of RORA polymorphism on the relationship between PTSD symptom severity and cortical structural changes.
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Affiliation(s)
- Rongfeng Qi
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Zhihong Cao
- Department of Radiology, the Affiliated Yixing Hospital of Jiangsu University, Wuxi, 75 Tongzhenguan Road, 214200 Wuxi, China
| | - Wesley Surento
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Li Zhang
- Mental Health Institute, the Second Xiangya Hospital, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan 410011, China
| | - Lianli Qiu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Zhuoman Xia
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Yan Yin
- Hangzhou Seventh People's Hospital, Mental Health Center of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310013, China
| | - Long Jiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Lingjiang Li
- Mental Health Institute, the Second Xiangya Hospital, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan 410011, China
| | - Yifeng Luo
- Department of Radiology, the Affiliated Yixing Hospital of Jiangsu University, Wuxi, 75 Tongzhenguan Road, 214200 Wuxi, China.
| | - Guang Ming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China.
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4
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Berto S, Treacher AH, Caglayan E, Luo D, Haney JR, Gandal MJ, Geschwind DH, Montillo AA, Konopka G. Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder. Nat Commun 2022; 13:3328. [PMID: 35680911 PMCID: PMC9184501 DOI: 10.1038/s41467-022-31053-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/13/2022] [Indexed: 12/13/2022] Open
Abstract
Gene expression covaries with brain activity as measured by resting state functional magnetic resonance imaging (MRI). However, it is unclear how genomic differences driven by disease state can affect this relationship. Here, we integrate from the ABIDE I and II imaging cohorts with datasets of gene expression in brains of neurotypical individuals and individuals with autism spectrum disorder (ASD) with regionally matched brain activity measurements from fMRI datasets. We identify genes linked with brain activity whose association is disrupted in ASD. We identified a subset of genes that showed a differential developmental trajectory in individuals with ASD compared with controls. These genes are enriched in voltage-gated ion channels and inhibitory neurons, pointing to excitation-inhibition imbalance in ASD. We further assessed differences at the regional level showing that the primary visual cortex is the most affected region in ASD. Our results link disrupted brain expression patterns of individuals with ASD to brain activity and show developmental, cell type, and regional enrichment of activity linked genes. Gene expression patterns have been associated with functional activity patterns in the brain. Here the authors determine how gene expression patterns in the human brain supports brain phenotypes obtained from resting state fMRI imaging, identifying brain regions and genes relevant to autism.
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Affiliation(s)
- Stefano Berto
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Alex H Treacher
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Emre Caglayan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Danni Luo
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jillian R Haney
- Program in Neurobehavioral Genetics, Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Program in Neurogenetics, Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael J Gandal
- Program in Neurobehavioral Genetics, Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Program in Neurogenetics, Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel H Geschwind
- Program in Neurobehavioral Genetics, Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Program in Neurogenetics, Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Albert A Montillo
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA. .,Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA. .,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
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5
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Ma L, Cunningham KA, Anastasio NC, Bjork JM, Taylor BA, Arias AJ, Riley BP, Snyder AD, Moeller FG. A serotonergic biobehavioral signature differentiates cocaine use disorder participants administered mirtazapine. Transl Psychiatry 2022; 12:187. [PMID: 35523779 PMCID: PMC9076859 DOI: 10.1038/s41398-022-01934-w] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 11/18/2022] Open
Abstract
Cocaine use disorder (CUD) patients display heterogenous symptoms and unforeseeable responses to available treatment approaches, highlighting the need to identify objective, accessible biobehavioral signatures to predict clinical trial success in this population. In the present experiments, we employed a task-based behavioral and pharmacogenetic-fMRI approach to address this gap. Craving, an intense desire to take cocaine, can be evoked by exposure to cocaine-associated stimuli which can trigger relapse during attempted recovery. Attentional bias towards cocaine-associated words is linked to enhanced effective connectivity (EC) from the anterior cingulate cortex (ACC) to hippocampus in CUD participants, an observation which was replicated in a new cohort of participants in the present studies. Serotonin regulates attentional bias to cocaine and the serotonergic antagonist mirtazapine decreased activated EC associated with attentional bias, with greater effectiveness in those CUD participants carrying the wild-type 5-HT2CR gene relative to a 5-HT2CR single nucleotide polymorphism (rs6318). These data suggest that the wild-type 5-HT2CR is necessary for the efficacy of mirtazapine to decrease activated EC in CUD participants and that mirtazapine may serve as an abstinence enhancer to mitigate brain substrates of craving in response to cocaine-associated stimuli in participants with this pharmacogenetic descriptor. These results are distinctive in outlining a richer "fingerprint" of the complex neurocircuitry, behavior and pharmacogenetics profile of CUD participants which may provide insight into success of future medications development projects.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States.
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States.
| | - Kathryn A Cunningham
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States.
| | - Noelle C Anastasio
- Center for Addiction Research and Department of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, TX, United States
| | - James M Bjork
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Brian A Taylor
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, United States
| | - Albert J Arias
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Brien P Riley
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Andrew D Snyder
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
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6
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Xin Y, Sheng J, Miao M, Wang L, Yang Z, Huang H. A review ofimaging genetics in Alzheimer's disease. J Clin Neurosci 2022; 100:155-163. [PMID: 35487021 DOI: 10.1016/j.jocn.2022.04.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/01/2022] [Accepted: 04/15/2022] [Indexed: 01/18/2023]
Abstract
Determining the association between genetic variation and phenotype is a key step to study the mechanism of Alzheimer's disease (AD), laying the foundation for studying drug therapies and biomarkers. AD is the most common type of dementia in the aged population. At present, three early-onset AD genes (APP, PSEN1, PSEN2) and one late-onset AD susceptibility gene apolipoprotein E (APOE) have been determined. However, the pathogenesis of AD remains unknown. Imaging genetics, an emerging interdisciplinary field, is able to reveal the complex mechanisms from the genetic level to human cognition and mental disorders via macroscopic intermediates. This paper reviews methods of establishing genotype-phenotype to explore correlations, including sparse canonical correlation analysis, sparse reduced rank regression, sparse partial least squares and so on. We found that most research work did poorly in supervised learning and exploring the nonlinear relationship between SNP-QT.
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Affiliation(s)
- Yu Xin
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China
| | - Jinhua Sheng
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
| | - Miao Miao
- Beijing Hospital, Beijing 100730, China; National Center of Gerontology, Beijing 100730, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Luyun Wang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; Hangzhou Vocational & Technical College, Hangzhou, Zhejiang 310018, China
| | - Ze Yang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China
| | - He Huang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China
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7
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DeLisi LE. Redefining schizophrenia through genetics: A commentary on 50 years searching for biological causes. Schizophr Res 2022; 242:22-24. [PMID: 34872835 DOI: 10.1016/j.schres.2021.11.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/14/2021] [Accepted: 11/15/2021] [Indexed: 11/24/2022]
Affiliation(s)
- Lynn E DeLisi
- Staff Psychiatrist, Cambridge Health Alliance, United States of America; Director of Faculty Affairs, Department of Psychiatry, Cambridge Health Alliance, United States of America; Professor of Psychiatry, Harvard Medical School, United States of America.
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8
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Kim M, Min EJ, Liu K, Yan J, Saykin AJ, Moore JH, Long Q, Shen L. Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics. Med Image Anal 2022; 76:102297. [PMID: 34871929 PMCID: PMC8792314 DOI: 10.1016/j.media.2021.102297] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/08/2021] [Accepted: 10/29/2021] [Indexed: 02/03/2023]
Abstract
The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer's or Parkinson's disease to demonstrate the generalizability of our method.
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Affiliation(s)
- Mansu Kim
- Department of Artificial Intelligence, Catholic University of Korea, Bucheon, Republic of Korea
| | - Eun Jeong Min
- College of Medicine, Catholic University of Korea, Seoul, Republic of Korea
| | - Kefei Liu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University, IN, USA
| | | | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA.
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9
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Won JH, Youn J, Park H. Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge. Med Image Anal 2022; 77:102378. [DOI: 10.1016/j.media.2022.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/29/2021] [Accepted: 01/26/2022] [Indexed: 11/28/2022]
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10
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Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints. Med Biol Eng Comput 2021; 60:95-108. [PMID: 34714488 DOI: 10.1007/s11517-021-02439-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/02/2021] [Indexed: 10/20/2022]
Abstract
Imaging genetics research can explore the potential correlation between imaging and genomics. Most association analysis methods cannot effectively use the prior knowledge of the original data. In this respect, we add the prior knowledge of each original data to mine more effective biomarkers. The study of imaging genetics based on the sparse canonical correlation analysis (SCCA) is helpful to mine the potential biomarkers of neurological diseases. To improve the performance and interpretability of SCCA, we proposed a penalty method based on the autocorrelation matrix for discovering the possible biological mechanism between single nucleotide polymorphisms (SNP) variations and brain regions changes of Alzheimer's disease (AD). The addition of the penalty allows the proposed algorithm to analyze the correlation between different modal features. The proposed algorithm obtains more biologically interpretable ROIs and SNPs that are significantly related to AD, which has better anti-noise performance. Compared with other SCCA-based algorithms (JCB-SCCA, JSNMNMF), the proposed algorithm can still maintain a stronger correlation with ground truth even when the noise is larger. Then, we put the regions of interest (ROI) selected by the three algorithms into the SVM classifier. The proposed algorithm has higher classification accuracy. Also, we use ridge regression with SNPs selected by three algorithms and four AD risk ROIs. The proposed algorithm has a smaller root mean square error (RMSE). It shows that proposed algorithm has a good ability in association recognition and feature selection. Furthermore, it selects important features more stably, improving the clinical diagnosis of new potential biomarkers.
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11
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Identifying Biomarkers of Alzheimer's Disease via a Novel Structured Sparse Canonical Correlation Analysis Approach. J Mol Neurosci 2021; 72:323-335. [PMID: 34570360 DOI: 10.1007/s12031-021-01915-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/09/2021] [Indexed: 02/05/2023]
Abstract
Using correlation analysis to study the potential connection between brain genetics and imaging has become an effective method to understand neurodegenerative diseases. Sparse canonical correlation analysis (SCCA) makes it possible to study high-dimensional genetic information. The traditional SCCA methods can only process single-modal genetic and image data, which to some extent weaken the close connection of the brain's biological network. In some recently proposed multimodal SCCA methods, due to the limitations of penalty items, the pre-processed data needs to be further filtered to make the dimensions uniform, which may destroy the potential association of data in the same modal. In this research, in order to combine data between different modalities and to ensure that the chain relationship or graph network relationship within the same modality will not be destroyed, the original generalized fused lasso penalty was replaced with the fused pairwise group lasso (FGL) and the graph-guided pairwise group lasso (GGL) based on the method of joint sparse canonical correlation analysis (JSCCA). We used prior knowledge to construct a supervised bivariate learning model and use linear regression to select quantitative traits (QTs) of images that are strongly correlated with the Mini-mental State Examination (MMSE) scores. Compared with FGL-SCCA, the model we constructed obtained a higher gene-ROI correlation coefficient and identified more significant biomarkers, providing a theoretical basis for further understanding the complex pathology of neurodegenerative diseases.
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Gao B, Feng C, Chai F, Wei S, Hong N, Ye Y, Wang Y, Cheng J. CT-detected extramural venous invasion-related gene signature for the overall survival prediction in patients with gastric cancer. Cancer Med 2021; 10:7816-7830. [PMID: 34510798 PMCID: PMC8559479 DOI: 10.1002/cam4.4266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/25/2021] [Accepted: 09/01/2021] [Indexed: 12/17/2022] Open
Abstract
Background Computed tomography (CT)‐detected extramural venous invasion (EMVI) has been identified as an independent factor that can be used for risk stratification and prediction of prognosis in patients with gastric cancer (GC). Overall survival (OS) is identified as the most important prognostic indicator for GC patients. However, the molecular mechanism of EMVI development and its potential relationship with OS in GC are not fully understood. In this radiogenomics‐based study, we sought to investigate the molecular mechanism underlying CT‐detected EMVI in patients with GC, and aimed to construct a genomic signature based on EMVI‐related genes with the goal of using this signature to predict the OS. Materials and Methods Whole mRNA genome sequencing of frozen tumor samples from 13 locally advanced GC patients was performed to identify EMVI‐related genes. EMVI‐prognostic hub genes were selected based on overlapping EMVI‐related differentially expressed genes and OS‐related genes, using a training cohort of 176 GC patients who were included in The Cancer Genome Atlas database. Another 174 GC patients from this database comprised the external validation cohort. A risk stratification model using a seven‐gene signature was constructed through the use of a least absolute shrinkage and selection operator Cox regression model. Results Patients with high risk score showed significantly reduced OS (training cohort, p = 1.143e‐04; validation cohort, p = 2.429e‐02). Risk score was an independent predictor of OS in multivariate Cox regression analyses (training cohort, HR = 2.758; 95% CI: 1.825–4.169; validation cohort, HR = 2.173; 95% CI: 1.347–3.505; p < 0.001 for both). Gene functions/pathways of the seven‐gene signature mainly included cell proliferation, cell adhesion, regulation of metal ion transport, and epithelial to mesenchymal transition. Conclusions A CT‐detected EMVI‐related gene model could be used to predict the prognosis in GC patients, potentially providing clinicians with additional information regarding appropriate therapeutic strategy and medical decision‐making.
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Affiliation(s)
- Bo Gao
- Department of General Surgery, Peking University People's Hospital, Beijing, China
| | - Caizhen Feng
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Fan Chai
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Shengcai Wei
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Yingjiang Ye
- Department of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, China
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, Beijing, China
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13
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Bi XA, Zhou W, Li L, Xing Z. Detecting Risk Gene and Pathogenic Brain Region in EMCI Using a Novel GERF Algorithm Based on Brain Imaging and Genetic Data. IEEE J Biomed Health Inform 2021; 25:3019-3028. [PMID: 33750717 DOI: 10.1109/jbhi.2021.3067798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.
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Chen Y, Herrold AA, Walter AE, Reilly JL, Seidenberg PH, Nauman EA, Talavage T, Vandenbergh DJ, Slobounov SM, Breiter HC. Brain Perfusion Bridges Virtual-Reality Spatial Behavior to TPH2 Genotype for Head Acceleration Events. J Neurotrauma 2021; 38:1368-1376. [PMID: 33413020 DOI: 10.1089/neu.2020.7016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging demonstrates that athletes of collision sports can suffer significant changes to their brain in the absence of concussion, attributable to head acceleration event (HAE) exposure. In a sample of 24 male Division I collegiate football players, we examine the relationships between tryptophan hydroxylase 2 (TPH2), a gene involved in neurovascular function, regional cerebral blood flow (rCBF) measured by arterial spin labeling, and virtual reality (VR) motor performance, both pre-season and across a single football season. For the pre-season, TPH2 T-carriers showed lower rCBF in two left hemisphere foci (fusiform gyrus/thalamus/hippocampus and cerebellum) in association with higher (better performance) VR Reaction Time, a dynamic measure of sensory-motor reactivity and efficiency of visual-spatial processing. For TPH2 CC homozygotes, higher pre-season rCBF in these foci was associated with better performance on VR Reaction Time. A similar relationship was observed across the season, where TPH2 T-carriers showed improved VR Reaction Time associated with decreases in rCBF in the right hippocampus/amygdala, left middle temporal lobe, and left insula/putamen/pallidum. In contrast, TPH2 CC homozygotes showed improved VR Reaction Time associated with increases in rCBF in the same three clusters. These findings show that TPH2 T-carriers have an abnormal relationship between rCBF and the efficiency of visual-spatial processing that is exacerbated after a season of high-impact sports in the absence of diagnosable concussion. Such gene-environment interactions associated with behavioral changes after exposure to repetitive HAEs have been unrecognized with current clinical analytical tools and warrant further investigation. Our results demonstrate the importance of considering neurovascular factors along with traumatic axonal injury to study long-term effects of repetitive HAEs.
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Affiliation(s)
- Yufen Chen
- Center for Translational Imaging, Department of Radiology, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Amy A Herrold
- Edward Hines Jr., VA Hospital, Research Service, Hines, Illinois, USA.,Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Alexa E Walter
- Department of Kinesiology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - James L Reilly
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Peter H Seidenberg
- Departments of Orthopedics and Rehabilitation and Family and Community Medicine, College of Medicine, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Eric A Nauman
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA.,Department of Basic Medical Sciences, Purdue University, West Lafayette, Indiana, USA.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Thomas Talavage
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - David J Vandenbergh
- Department of Biobehavioral Health, Pennsylvania State University, University Park, Pennsylvania, USA.,Penn State Neuroscience Institute, Pennsylvania State University, University Park, Pennsylvania, USA.,Molecular, Cellular, and Integrative Biosciences Program, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Semyon M Slobounov
- Department of Kinesiology, Pennsylvania State University, University Park, Pennsylvania, USA
| | - Hans C Breiter
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.,Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
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15
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Chen Z, Tao S, Zhu R, Tian S, Sun Y, Wang H, Yan R, Shao J, Zhang Y, Zhang J, Yao Z, Lu Q. Aberrant functional connectivity between the suprachiasmatic nucleus and the superior temporal gyrus: Bridging RORA gene polymorphism with diurnal mood variation in major depressive disorder. J Psychiatr Res 2021; 132:123-130. [PMID: 33091686 DOI: 10.1016/j.jpsychires.2020.09.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 09/24/2020] [Accepted: 09/30/2020] [Indexed: 10/23/2022]
Abstract
Diurnal mood variation (DMV), a common symptom of major depressive disorder (MDD), is associated with circadian related genes and dysregulation of the suprachiasmatic nucleus (SCN). Previous research confirmed that the RORA gene is involved in the regulation of circadian rhythms. In this study, we hypothesized that polymorphisms of RORA may affect DMV symptoms of MDD through functional changes in the SCN. A total of 208 patients diagnosed with depression and 120 control subjects were enrolled and underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Blood samples were collected and genotyping of 9 RORA gene SNPs were performed using next-generation sequencing technology. Patients were categorized as an AA genotype or C allele carriers based on RORA rs72752802 polymorphism. SCN-seed functional connectivity (FC) was compared between the two groups and correlation with severity of DMV was analyzed. Finally, a mediation analysis was performed to further determine FC intermediary effects. We observed that rs72752802 was significantly associated with patients' DMV symptoms. C allele carriers of rs72752802 showed significantly decreased FC between the right SCN and right superior temporal gyrus (rSTG). This was also correlated with DMV symptoms. In addition, the rs72752802 SNP influenced DMV symptoms through intermediary effects of SCN-rSTG connectivity. The study presented here provides a neurological and genetic basis for understanding depressed patients experiencing DMV.
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Affiliation(s)
- Zhilu Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shiwan Tao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rongxin Zhu
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, 210096, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, 210096, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, 210096, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, 210096, China
| | - Yujie Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, 210096, China
| | - Jie Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China; School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, 210096, China.
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Palk A, Illes J, Thompson PM, Stein DJ. Ethical issues in global neuroimaging genetics collaborations. Neuroimage 2020; 221:117208. [PMID: 32736000 DOI: 10.1016/j.neuroimage.2020.117208] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 06/09/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022] Open
Abstract
Neuroimaging genetics is a rapidly developing field that combines neuropsychiatric genetics studies with imaging modalities to investigate how genetic variation influences brain structure and function. As both genetic and imaging technologies improve further, their combined power may hold translational potential in terms of improving psychiatric nosology, diagnosis, and treatment. While neuroimaging genetics studies offer a number of scientific advantages, they also face challenges. In response to some of these challenges, global neuroimaging genetics collaborations have been created to pool and compare brain data and replicate study findings. Attention has been paid to ethical issues in genetics, neuroimaging, and multi-site collaborative research, respectively, but there have been few substantive discussions of the ethical issues generated by the confluence of these areas in global neuroimaging genetics collaborations. Our discussion focuses on two areas: benefits and risks of global neuroimaging genetics collaborations and the potential impact of neuroimaging genetics research findings in low- and middle-income countries. Global neuroimaging genetics collaborations have the potential to enhance relations between countries and address global mental health challenges, however there are risks regarding inequity, exploitation and data sharing. Moreover, neuroimaging genetics research in low- and middle-income countries must address the issue of feedback of findings and the risk of essentializing and stigmatizing interpretations of mental disorders. We conclude by examining how the notion of solidarity, informed by an African Ethics framework, may justify some of the suggestions made in our discussion.
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Affiliation(s)
- Andrea Palk
- Department of Philosophy, Stellenbosch University, Bag X1, Matieland, Stellenbosch, 7602, South Africa.
| | - Judy Illes
- Neuroethics Canada, Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Groote Schuur Hospital, Cape Town 7925, South Africa
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17
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Qi R, Luo Y, Zhang L, Weng Y, Surento W, Jahanshad N, Xu Q, Yin Y, Li L, Cao Z, Thompson PM, Lu GM. FKBP5 haplotypes and PTSD modulate the resting-state brain activity in Han Chinese adults who lost their only child. Transl Psychiatry 2020; 10:91. [PMID: 32170058 PMCID: PMC7070023 DOI: 10.1038/s41398-020-0770-5] [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: 10/25/2019] [Revised: 02/19/2020] [Accepted: 02/24/2020] [Indexed: 12/19/2022] Open
Abstract
The stress-related gene FKBP5 has been related to dysregulated glucocorticoid receptor (GR) signaling, showing increased GR sensitivity in trauma-exposed subjects with post-traumatic stress disorder (PTSD) but not in those without PTSD. However, the neural mechanism underlying the effects of FKBP5 remains poorly understood. Two hundred and thirty-seven Han Chinese adults who had lost their only child were included. Four FKBP5 single nucleotide polymorphisms (rs3800373, rs9296158, rs1360780, and rs9470080) were genotyped. All 179 participants were successfully divided into three FKBP5 diplotype subgroups according to two major FKBP5 H1 and H2 yin yang haplotypes. Brain average spectral power was compared using a two-way (PTSD diagnosis and FKBP5 diplotypes) analysis of covariance within four separate frequency bands (slow-5, slow-4, slow-3, and slow-2). Adults with PTSD showed lower spectral power in bilateral parietal lobules in slow-4 and in left inferior frontal gyrus (IFG) in slow-5. There was significant FKBP5 diplotype main effect in anterior cingulate cortex (ACC) in slow-4 (H1/H1 higher than other two subgroups), and in precentral/postcentral gyri and middle cingulate cortex (MCC) in slow-3 (H2/H2 higher than other two subgroups). Also, there was a significant diagnosis × FKBP5 diplotype interaction effect in right parietal lobule in slow-3. These findings suggest that adults with PTSD have lower low-frequency power in executive control network regions. Lower power in ACC and greater power in the motor/sensory areas in FKBP5 high-risk diplotype group suggest a disturbance of emotional processing and hypervigilance/sensitization to threatening stimuli. The interaction effect of diagnosis × FKBP5 in parietal lobule may contribute to PTSD development.
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Affiliation(s)
- Rongfeng Qi
- grid.41156.370000 0001 2314 964XDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu China ,grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292 USA
| | - Yifeng Luo
- Department of Radiology, The Affiliated Yixing Hospital of Jiangsu University, 75 Tongzhenguan Road, 214200 Wuxi, China
| | - Li Zhang
- grid.216417.70000 0001 0379 7164Mental Health Institute of the Second Xiangya Hospital, Central South University, China National Clinical Research Center for Mental Health Disorders, National Technology Institute of Psychiatry, 139 Middle Renmin Road, 410011 Changsha, Hunan China
| | - Yifei Weng
- grid.41156.370000 0001 2314 964XDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu China
| | - Wesley Surento
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292 USA
| | - Neda Jahanshad
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292 USA
| | - Qiang Xu
- grid.41156.370000 0001 2314 964XDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 210002 Nanjing, Jiangsu China
| | - Yan Yin
- Psychology Department, Hangzhou Seventh People’s Hospital, 310013 Hangzhou, Zhejiang China
| | - Lingjiang Li
- grid.216417.70000 0001 0379 7164Mental Health Institute of the Second Xiangya Hospital, Central South University, China National Clinical Research Center for Mental Health Disorders, National Technology Institute of Psychiatry, 139 Middle Renmin Road, 410011 Changsha, Hunan China
| | - Zhihong Cao
- Department of Radiology, The Affiliated Yixing Hospital of Jiangsu University, 75 Tongzhenguan Road, 214200 Wuxi, China
| | - Paul M. Thompson
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA 90292 USA
| | - Guang Ming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 210002, Nanjing, Jiangsu, China.
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Zhang Y, Peng P, Ju Y, Li G, Calhoun VD, Wang YP. Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity. IEEE J Biomed Health Inform 2020; 24:2621-2629. [PMID: 32071012 DOI: 10.1109/jbhi.2020.2972581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real imaging genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.
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Effects of COMT rs4680 and BDNF rs6265 polymorphisms on brain degree centrality in Han Chinese adults who lost their only child. Transl Psychiatry 2020; 10:46. [PMID: 32066722 PMCID: PMC7026113 DOI: 10.1038/s41398-020-0728-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 01/13/2020] [Accepted: 01/14/2020] [Indexed: 11/25/2022] Open
Abstract
Losing one's only child is a major traumatic life event that may lead to posttraumatic stress disorder (PTSD); however, not all parents who experience this trauma develop PTSD. Genetic variants are associated with the risk of developing PTSD. Catechol-O-methyltransferase (COMT) rs4680 and brain-derived neurotrophic factor (BDNF) rs6265 are two most well-described single-nucleotide polymorphisms that relate to stress response; however, the neural mechanism underlying their effects on adults who lost an only child remains poorly understood. Two hundred and ten Han Chinese adults who had lost their only child (55 with PTSD and 155 without PTSD) were included in this imaging genetics study. Participants were divided into subgroups according to their COMT rs4680 and BDNF rs6265 genotypes. Degree Centrality (DC)-a resting-state fMRI index reflecting the brain network communication-was compared with a three-way (PTSD diagnosis, COMT, and BDNF polymorphisms) analysis of covariance. Diagnosis state had a significant effect on DC in bilateral inferior parietal lobules and right middle frontal gyrus (MFG), where PTSD adults showed weaker DC. BDNF × diagnosis interaction effect was found in the right MFG and hippocampus, and these two regions were reversely modulated. Also, there was a significant COMT × BDNF interaction effect in left cuneus, middle temporal gyrus, right inferior occipital gyrus, and bilateral putamen, independent of PTSD diagnosis. These findings suggest that the modulatory effect of BDNF polymorphism on the MFG and hippocampus may contribute to PTSD development in bereaved adults. Interactions of COMT × BDNF polymorphisms modulate some cortices and basal ganglia, irrespective of PTSD development.
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Kim M, Won JH, Youn J, Park H. Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:23-34. [PMID: 31144631 DOI: 10.1109/tmi.2019.2918839] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson's disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared with other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer's patients or healthy subjects to determine the generalizability of our algorithm.
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TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review. Int J Mol Sci 2019; 20:ijms20236033. [PMID: 31795520 PMCID: PMC6929079 DOI: 10.3390/ijms20236033] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 11/26/2019] [Accepted: 11/28/2019] [Indexed: 12/11/2022] Open
Abstract
In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular 'omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular 'omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular 'omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.
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Zanfardino M, Franzese M, Pane K, Cavaliere C, Monti S, Esposito G, Salvatore M, Aiello M. Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases. J Transl Med 2019; 17:337. [PMID: 31590671 PMCID: PMC6778975 DOI: 10.1186/s12967-019-2073-2] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/18/2019] [Indexed: 02/07/2023] Open
Abstract
Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.
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Fischer S, Tahoun M, Klaan B, Thierfelder KM, Weber MA, Krause BJ, Hakenberg O, Fuellen G, Hamed M. A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer. Cancers (Basel) 2019; 11:E1293. [PMID: 31480766 PMCID: PMC6770738 DOI: 10.3390/cancers11091293] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 08/20/2019] [Accepted: 08/28/2019] [Indexed: 12/28/2022] Open
Abstract
Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.
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Affiliation(s)
- Sarah Fischer
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, 18057 Rostock, Germany
| | - Mohamed Tahoun
- Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt
| | - Bastian Klaan
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, 18057 Rostock, Germany
| | - Kolja M Thierfelder
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, 18057 Rostock, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, 18057 Rostock, Germany
| | - Bernd J Krause
- Department of Nuclear Medicine, Rostock University Medical Center, 18057 Rostock, Germany
| | - Oliver Hakenberg
- Department of Urology, Rostock University Medical Center, 18057 Rostock, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, 18057 Rostock, Germany
| | - Mohamed Hamed
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, 18057 Rostock, Germany.
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Antonelli L, Guarracino MR, Maddalena L, Sangiovanni M. Integrating imaging and omics data: A review. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.032] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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Tao B, Xiao Y, Hu N, Shah C, Liu L, Gao X, Liu J, Zhang W, Yao L, Xu H, Hua J, Lui S. Reduced cortical thickness related to single nucleotide polymorphisms in the major histocompatibility complex region in antipsychotic-naive schizophrenia. Brain Behav 2019; 9:e01253. [PMID: 30924326 PMCID: PMC6598395 DOI: 10.1002/brb3.1253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 01/30/2019] [Accepted: 02/13/2019] [Indexed: 02/05/2023] Open
Abstract
The aim of this study was to explore the relationships between changes in cortical thickness and single nucleotide polymorphisms (SNPs) in the major histocompatibility complex (MHC) region in a group of antipsychotic-naive schizophrenia (AN-SCZ) patients. Methods Twenty-five AN-SCZ patients and 51 healthy controls (HCs) participated in this study. General linear models were used to identify associations between the average cortical thicknesses of each brain region (N = 68) and each of the 11 SNPs in the MHC region in the AN-SCZ patients and HCs. Next, we performed independent-sample t tests to investigate whether cortical thickness was significantly lower in the AN-SCZ patients than in HCs in the brain regions that were significantly associated with the SNPs. Finally, we examined the correlations between clinical symptoms and cortical thickness in the above brain areas in the whole AN-SCZ group using Pearson correlation tests. Results Seven of the 11 SNPs within the MHC region were significantly associated with cortical thickness only in the AN-SCZ patients; these included rs1635, rs1736913, rs2021722, rs204999, rs2523722, rs3131296, and rs9272105. The AN-SCZ patients had significantly thinner cortical thicknesses in the above brain regions, especially the prefrontal cortex. Furthermore, the left entorhinal region was negatively correlated with Positive and Negative Symptom Scale (PANSS) activation scores in the AN-SCZ group (r = -0.601, p = 0.03). Conclusions This study provides evidence demonstrating the potential effects of MHC risk variants in cortical thickness deficits in AN-SCZ. These data also support the notion that the immune system plays critical roles in the pathology of schizophrenia, which is mediated via the modulation of the development of cerebral cortical structures.
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Affiliation(s)
- Bo Tao
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Chandan Shah
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Lu Liu
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Xin Gao
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Jieke Liu
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Li Yao
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
| | - Heng Xu
- State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| | - Jun Hua
- Department of Radiology, Johns Hopkins University of Medicine, Baltimore, Maryland
| | - Su Lui
- Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China
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Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage 2019; 189:516-532. [PMID: 30708106 PMCID: PMC6462481 DOI: 10.1016/j.neuroimage.2019.01.068] [Citation(s) in RCA: 161] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 01/22/2019] [Accepted: 01/27/2019] [Indexed: 01/15/2023] Open
Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA.
| | - Annchen R Knodt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - Megan Cooke
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - M Justin Kim
- Department of Psychology, University of Hawaii at Manoa, Honolulu, HI, 96822, USA
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Ross Keenan
- New Zealand Brain Research Institute, Christchurch, New Zealand; Christchurch Radiology Group, Christchurch, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
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Lischke A, Pahnke R, König J, Homuth G, Hamm AO, Wendt J. COMTVal158Met Genotype Affects Complex Emotion Recognition in Healthy Men and Women. Front Neurosci 2019; 12:1007. [PMID: 30723391 PMCID: PMC6349699 DOI: 10.3389/fnins.2018.01007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 12/13/2018] [Indexed: 11/15/2022] Open
Abstract
The catechol-o-methyltransferase (COMT) gene has repeatedly been shown to change amygdala activity and amygdala-prefrontal connectivity during face processing. Although the COMT gene appears to induce a negativity bias during the neural processing of faces, it is currently unclear whether a similar negativity bias emerges during the behavioral processing of faces. To address this issue, we investigated differences in complex emotion recognition between participants (n = 181) that had been a priori genotyped for functional polymorphisms of the COMT (Val158Met) and serotonin transporter (5-HTTLPR) gene. We were, thus, able to analyze differences in face processing on basis of participants’ COMT genotype while controlling for participants’ 5-HTTLPR genotype. Variations of participants’ COMT but not 5-HTTLPR genotype accounted for differences in participants’ emotion recognition performance: Met/Met carriers and Met/Val carriers were more accurate in the recognition of negative, but not neutral or positive, expressions than Val/Val carriers. We, therefore, revealed a similar negativity bias during the behavioral processing of faces that has already been demonstrated during the neural processing of faces, indicating that genotype-dependent changes in catecholamine metabolism may affect face processing on the behavioral and neural level.
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Affiliation(s)
- Alexander Lischke
- Department of Psychology, University of Greifswald, Greifswald, Germany
| | - Rike Pahnke
- Institute of Sport Sciences, University of Rostock, Rostock, Germany
| | - Jörg König
- Department of Psychology, University of Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, University of Greifswald, Greifswald, Germany
| | - Alfons O Hamm
- Department of Psychology, University of Greifswald, Greifswald, Germany
| | - Julia Wendt
- Department of Psychology, University of Greifswald, Greifswald, Germany
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Abstract
Research in cultural neuroscience and development examines the processes and mechanisms underlying the interaction of cultural systems with environmental and biological systems with a life course approach. Culture interacts with environmental and biological factors to shape the mind, brain and behavior across stages of development. Theoretical and empirical approaches in cultural neuroscience investigate how culture influences psychological and neurobiological mechanisms during developmental periods. Methodological approaches in cultural neuroscience and development illustrate the opportunities and challenges with observation and measurement of psychological and biological processes across cultures throughout development. This review examines empirical findings in cultural neuroscience on emotional, cognitive and social development. Implications of theoretical and methodological advances in cultural neuroscience and development for global mental health will be discussed.
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Tao S, Chattun MR, Yan R, Geng J, Zhu R, Shao J, Lu Q, Yao Z. TPH-2 Gene Polymorphism in Major Depressive Disorder Patients With Early-Wakening Symptom. Front Neurosci 2018; 12:827. [PMID: 30519155 PMCID: PMC6251472 DOI: 10.3389/fnins.2018.00827] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 10/23/2018] [Indexed: 12/13/2022] Open
Abstract
Background: Sleep disturbances, such as early wakening, are frequently observed in patients with major depressive disorder (MDD). The suprachiasmatic nuclei (SCN), which controls circadian rhythm, is innervated by the raphe nucleus, a region where Tryptophan hydroxylase-2 (TPH-2) gene is primarily expressed. Although TPH-2 is often implicated in the pathophysiology of depression, few studies have applied a genetic and imaging technique to investigate the mechanism of early wakening symptom in MDD. We hypothesized that TPH-2 variants could influence the function of SCN in MDD patients with early wakening symptom. Methods: One hundred and eighty five MDD patients (62 patients without early wakening and 123 patients with early wakening) and 64 healthy controls participated in this study. Blood samples were collected and genotyping of rs4290270, rs4570625, rs11178998, rs7305115, rs41317118, and rs17110747 were performed by next-generation sequencing (NGS) technology. Logistic regression model was employed for genetic data analysis using the PLINK software. Based on the allele type, rs4290270, which was significant in the early wakening MDD group, participants were categorized into two groups (A allele and T carrier). All patients underwent whole brain resting-state functional magnetic resonance imaging (rs-fMRI) scanning and a voxel-wise functional connectivity comparison was performed between the groups. Results: rs4290270 was significantly linked to MDD patients who exhibited early wakening symptom. The functional connectivities of the right SCN with the right fusiform gyrus and right middle frontal gyrus were increased in the T carrier group compared to the A allele group. In addition, the functional connectivities of the left SCN with the right lingual gyrus and left calcarine sulcus were decreased in the T carrier group compared to the A allele group. Conclusion: These findings suggested that the TPH-2 gene variant, rs4290270, affected the circadian regulating function of SCN. The altered functional connectivities, observed between the SCN and right fusiform gyrus, right middle frontal gyrus, the right lingual gyrus and left calcarine sulcus, could highlight the neural mechanism by which SCN induces sleep-related circadian disruption in T carrier MDD patients. Hence, rs4290270 could potentially serve as a reliable biomarker to identify MDD patients with early wakening symptom.
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Affiliation(s)
- Shiwan Tao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Mohammad Ridwan Chattun
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiting Geng
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Medical School of Nanjing University, Nanjing Brain Hospital, Nanjing, China
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Gonda X, Petschner P, Eszlari N, Baksa D, Edes A, Antal P, Juhasz G, Bagdy G. Genetic variants in major depressive disorder: From pathophysiology to therapy. Pharmacol Ther 2018; 194:22-43. [PMID: 30189291 DOI: 10.1016/j.pharmthera.2018.09.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.
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Affiliation(s)
- Xenia Gonda
- Department of Psychiatry and Psychotherapy, Kutvolgyi Clinical Centre, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary.
| | - Peter Petschner
- MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Nora Eszlari
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Daniel Baksa
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Andrea Edes
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Peter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Neuroscience and Psychiatry Unit, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Gyorgy Bagdy
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.
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Deng SP, Hu W, Calhoun VD, Wang YP. Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1480-1491. [PMID: 28880187 PMCID: PMC6207076 DOI: 10.1109/tcbb.2017.2748944] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.
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Affiliation(s)
- Su-Ping Deng
- Department of Biomedical Engineering, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA.,
| | - Wenxing Hu
- Department of Biomedical Engineering, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA.,
| | | | - Yu-Ping Wang
- Department of Biomedical Engineering, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA., , Telephone: (504)865-5867, Fax: (504)862-8779
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Avinun R, Nevo A, Knodt AR, Elliott ML, Hariri AR. Replication in Imaging Genetics: The Case of Threat-Related Amygdala Reactivity. Biol Psychiatry 2018; 84:148-159. [PMID: 29279201 PMCID: PMC5955809 DOI: 10.1016/j.biopsych.2017.11.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/18/2017] [Accepted: 11/05/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND Low replication rates are a concern in most, if not all, scientific disciplines. In psychiatric genetics specifically, targeting intermediate brain phenotypes, which are more closely associated with putative genetic effects, was touted as a strategy leading to increased power and replicability. In the current study, we attempted to replicate previously published associations between single nucleotide polymorphisms and threat-related amygdala reactivity, which represents a robust brain phenotype not only implicated in the pathophysiology of multiple disorders, but also used as a biomarker of future risk. METHODS We conducted a literature search for published associations between single nucleotide polymorphisms and threat-related amygdala reactivity and found 37 unique findings. Our replication sample consisted of 1117 young adult volunteers (629 women, mean age 19.72 ± 1.25 years) for whom both genetic and functional magnetic resonance imaging data were available. RESULTS Of the 37 unique associations identified, only three replicated as previously reported. When exploratory analyses were conducted with different model parameters compared to the original findings, significant associations were identified for 28 additional studies: eight of these were for a different contrast/laterality; five for a different gender and/or race/ethnicity; and 15 in the opposite direction and for a different contrast, laterality, gender, and/or race/ethnicity. No significant associations, regardless of model parameters, were detected for six studies. Notably, none of the significant associations survived correction for multiple comparisons. CONCLUSIONS We discuss these patterns of poor replication with regard to the general strategy of targeting intermediate brain phenotypes in genetic association studies and the growing importance of advancing the replicability of imaging genetics findings.
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Affiliation(s)
- Reut Avinun
- Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, North Carolina.
| | - Adam Nevo
- Cardiothoracic Division, Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | - Annchen R. Knodt
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Maxwell L. Elliott
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Ahmad R. Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
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Bassir Nia A, Eveleth MC, Gabbay JM, Hassan YJ, Zhang B, Perez-Rodriguez MM. Past, present, and future of genetic research in borderline personality disorder. Curr Opin Psychol 2018; 21:60-68. [PMID: 29032046 PMCID: PMC5847441 DOI: 10.1016/j.copsyc.2017.09.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 08/24/2017] [Accepted: 09/05/2017] [Indexed: 01/19/2023]
Abstract
Borderline Personality Disorder (BPD) is a major mental illness with a lifetime prevalence of approximately 1-3%, characterized by a persistent pattern of instability in relationships, mood, impulse regulation, and sense of self. This results in impulsive self-damaging behavior, high suicide rates, and severe functional impairment. BPD has a complex, multifactorial etiology, resulting from an interaction among genetic and environmental substrates, and has moderate to high heritability based on twin and family studies. However, our understanding of the genetic architecture of BPD is very limited. This is a critical obstacle since genetics can pave the way for identifying new treatment targets and developing preventive and disease-modifying pharmacological treatments which are currently lacking. We review genetic studies in BPD, with a focus on limitations and challenges and future directions. Genetic research in BPD is still in its very early stages compared to other major psychiatric disorders. Most early genetic studies in BPD were non-replicated association studies in small samples, focused on single candidate genes. More recently, there has been one genome-wide linkage study and a genome-wide association study (GWAS) of subclinical BPD traits and a first GWAS in a relatively modest sample of patients fulfilling full diagnostic criteria for the disorder. Although there are adequate animal models for some of the core dimensions of BPD, there is a lack of translational research including data from animal models in BPD. Research in more pioneering fields, such as imaging genetics, deep sequencing and epigenetics, holds promise for elucidating the pathophysiology of BPD and identifying new treatment targets.
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Affiliation(s)
- Anahita Bassir Nia
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthew C Eveleth
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan M Gabbay
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yonis J Hassan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bosi Zhang
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - M Mercedes Perez-Rodriguez
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, Bronx, NY 10468, USA; CIBERSAM, Autonoma University, Fundacion Jimenez Diaz and Ramon y Cajal Hospital, Madrid, Spain.
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35
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Woolley J, McGuire P. Neuroimaging in schizophrenia: what does it tell the clinician? ACTA ACUST UNITED AC 2018. [DOI: 10.1192/apt.11.3.195] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Neuroimaging has been used in clinical practice for over 30 years, but it is still perceived as rarely offering the psychiatrist much help in direct patient management. As newer imaging modalities are introduced (from computed tomography and positron and single photon emission tomography to magnetic and functional magnetic resonance imaging), the promise of imminent clinical utility is reawakened, only to fade as the innovation is shown to be another, albeit useful, research tool. The aim of this article is to update readers on some recent advances that are starting to align the research and clinical functions of neuroimaging. As imaging becomes more accessible and affordable there is real promise that both clinicians and patients will begin to benefit more directly.
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Stoyanov D, Kandilarova S, Borgwardt S. Translational Functional Neuroimaging in the Explanation of Depression. Balkan Med J 2017; 34:493-503. [PMID: 29019461 PMCID: PMC5785653 DOI: 10.4274/balkanmedj.2017.1160] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Translation as a notion and procedure is deeply embodied in medical science and education. Translation includes the possibility to translate data across disciplines to improve diagnosis and treatment procedures. The evidence accumulated using translation serves as a vehicle for reification of medical diagnoses. There are promising, established post hoc correlations between the different types of clinical tools (interviews and inventories) and neuroscience. The various measures represent statistical correlations that must now be integrated into diagnostic standards and procedures but this, as a whole, is a step forward towards a better understanding of the mechanisms underlying psychopathology in general and depression in particular. Here, we focus on functional magnetic resonance imaging studies using a trans-disciplinary approach and attempt to establish bridges between the different fields. We will selectively highlight research areas such as imaging genetics, imaging immunology and multimodal imaging, as related to the diagnosis and management of depression.
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Affiliation(s)
- Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, Plovdiv, Bulgaria.,Research Complex for Translational Neuroscience, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, Plovdiv, Bulgaria.,Research Complex for Translational Neuroscience, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Stefan Borgwardt
- Department of Psychiatry, University of Basel, Basel, Switzerland
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Carter CS, Bearden CE, Bullmore ET, Geschwind DH, Glahn DC, Gur RE, Meyer-Lindenberg A, Weinberger DR. Enhancing the Informativeness and Replicability of Imaging Genomics Studies. Biol Psychiatry 2017; 82:157-164. [PMID: 27793332 PMCID: PMC5318285 DOI: 10.1016/j.biopsych.2016.08.019] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2014] [Revised: 11/10/2015] [Accepted: 08/17/2016] [Indexed: 01/10/2023]
Abstract
Imaging genomics is a new field of investigation that seeks to gain insights into the impact of human genetic variation on the structure, chemistry, and function of neural systems in health and disease. Because publications in this field have increased over the past decade, increasing concerns have been raised about false-positive results entering the literature. Here, we provide an overview of the field of imaging genomic and genetic approaches and discuss factors related to research design and analysis that can enhance the informativeness and replicability of these studies. We conclude that imaging genetic studies can provide important insights into the role of human genetic variation on neural systems and circuits, both in the context of normal quantitative variation and in relation to neuropsychiatric disease. We also argue that demonstrating genetic association to imaging-derived traits is subject to the same constraints as any other genetic study, including stringent type I error control. Adequately powered studies are necessary; however, there are currently limited data available to allow precise estimates of effect sizes for candidate gene studies. Independent replication is necessary before a result can be considered definitive, and for studies with small sample sizes it is necessary before publication. Increased transparency of methods and enhanced data sharing will further enhance replicability.
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Affiliation(s)
- Cameron S. Carter
- University of California at Davis, 4701 X Street Sacramento, CA 95816, phone 916 7348883 fax 916 7347884,
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Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, Monti S, Salvatore M. Radiogenomic Analysis of Oncological Data: A Technical Survey. Int J Mol Sci 2017; 18:ijms18040805. [PMID: 28417933 PMCID: PMC5412389 DOI: 10.3390/ijms18040805] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 04/06/2017] [Accepted: 04/08/2017] [Indexed: 12/18/2022] Open
Abstract
In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.
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Affiliation(s)
| | - Marco Aiello
- IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy.
| | | | | | | | | | - Serena Monti
- IRCCS SDN, Via E. Gianturco, 113, 80143 Naples, Italy.
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Volf NV, Belousova LV, Knyazev GG, Kulikov AV. Interactive effect of 5-HTTLPR genotype and age on sources of cortical rhythms in healthy women. Int J Psychophysiol 2016; 109:107-115. [PMID: 27616474 DOI: 10.1016/j.ijpsycho.2016.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 08/29/2016] [Accepted: 09/07/2016] [Indexed: 11/26/2022]
Abstract
This study was aimed to localize the effects of 5-HTTLPR (serotonin-transporter-linked polymorphic region) on the age differences of spontaneous EEG activity in women using neuroimaging analysis sLORETA (Standardized Low Resolution brain Electromagnetic Tomography). DNA samples extracted from cheek swabs and resting-state EEG recorded at 60 standard leads were collected from young (YW, N=86, 18-35years) and older (OW, N=45; 55-80years) healthy women. We have shown that advanced age was associated with increased posterior EEG desynchronization in S'/S'. S' (LG allele was grouped with S alleles owing to its functional equivalence and this group was labeled as S') genotype carriers denoted by decrease of delta - beta1 current source density, and to a lesser extent in L/L homozygotes denoted by decrease in delta activity. In heterozygotes OW, as compared with heterozygotes YW, higher source density estimates of beta1 in frontal and temporal cortex were observed. Age differences were more pronounced in the right hemisphere in S'/S' and L/L carriers and in the left hemisphere in heterozygotes. We also found that in OW, current source density estimates of theta, alpha1, alpha2, alpha3 and beta1 sources in the right occipital lobe were higher in S'/L than in S'/S' carriers. These results may have implications for understanding 5-HTT-dependent variation in the effect of aging on brain activity.
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Affiliation(s)
- Nina V Volf
- State Scientific-Research Institute of Physiology and Basic Medicine, Timakova Street 4, Novosibirsk 630117, Russia; Novosibirsk State University, Pirogova Street 2, Novosibirsk 630090, Russia
| | - Ludmila V Belousova
- State Scientific-Research Institute of Physiology and Basic Medicine, Timakova Street 4, Novosibirsk 630117, Russia.
| | - Gennady G Knyazev
- State Scientific-Research Institute of Physiology and Basic Medicine, Timakova Street 4, Novosibirsk 630117, Russia
| | - Alexander V Kulikov
- Novosibirsk State University, Pirogova Street 2, Novosibirsk 630090, Russia; Institute of Cytology and Genetics of Siberian Branch of Russian Academy of Sciences, Prospekt Lavrentyeva, 10, Novosibirsk 630090, Russia
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Chekouo T, Stingo FC, Guindani M, Do KA. A Bayesian predictive model for imaging genetics with application to schizophrenia. Ann Appl Stat 2016. [DOI: 10.1214/16-aoas948] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Congdon E, Canli T. The Endophenotype of Impulsivity: Reaching Consilience Through Behavioral, Genetic, and Neuroimaging Approaches. ACTA ACUST UNITED AC 2016; 4:262-81. [PMID: 16585800 DOI: 10.1177/1534582305285980] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Impulsivity is a multidimensional construct with implications for understanding the etiology and treatment of multiple forms of psychopathology. As a multidimensional construct, however, the processes underlying impulsivity, particularly behavioral inhibition, must be separated to allow for investigations into its neurogenetic bases. Evidence from both animal and human studies supports the role of dopamine in impulsivity, and neuroimaging research is elucidating brain regions involved in behavioral inhibition. Evidence is now emerging that suggests an interaction between dopamine system genes and frontal brain regions in underlying individual differences in behavioral inhibition. However, to reach a comprehensive understanding of the neurogenetic bases of behavioral inhibition, an appropriate framework is required. Therefore, it is proposed that by identifying intervening variables more sensitive to the effects of genetic variation, known as an endophenotype approach, we will be able to overcome many of the methodological limitations that prevent a better understanding at present.
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Affiliation(s)
- Eliza Congdon
- Department of Psychology, Stony Brook University, NY, USA
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42
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Tong Y, Chen Q, Nichols TE, Rasetti R, Callicott JH, Berman KF, Weinberger DR, Mattay VS. Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics. PLoS One 2016; 11:e0151391. [PMID: 26974435 PMCID: PMC4790904 DOI: 10.1371/journal.pone.0151391] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 02/27/2016] [Indexed: 11/21/2022] Open
Abstract
A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others.
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Affiliation(s)
- Yunxia Tong
- Clinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
| | - Thomas E. Nichols
- Department of Statistics, University of Warwick, Warwick, United Kingdom
| | - Roberta Rasetti
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Joseph H. Callicott
- Clinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Karen F. Berman
- Clinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
- Departments of Psychiatry, Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Venkata S. Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
- Departments of Neurology and Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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43
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Katrib A, Hsu W, Bui A, Xing Y. "RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment. QUANTITATIVE BIOLOGY 2016; 4:1-12. [PMID: 28529815 DOI: 10.1007/s40484-016-0061-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Recent advances in quantitative imaging and "omics" technology have generated a wealth of mineable biological "big data". With the push towards a P4 "predictive, preventive, personalized, and participatory" approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose "radiotranscriptomics" as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.
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Affiliation(s)
- Amal Katrib
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - William Hsu
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Alex Bui
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Xing
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
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44
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Franke B, Stein JL, Ripke S, Anttila V, Hibar DP, van Hulzen KJE, Arias-Vasquez A, Smoller JW, Nichols TE, Neale MC, McIntosh AM, Lee P, McMahon FJ, Meyer-Lindenberg A, Mattheisen M, Andreassen OA, Gruber O, Sachdev PS, Roiz-Santiañez R, Saykin AJ, Ehrlich S, Mather KA, Turner JA, Schwarz E, Thalamuthu A, Shugart YY, Ho YYW, Martin NG, Wright MJ, O'Donovan MC, Thompson PM, Neale BM, Medland SE, Sullivan PF. Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept. Nat Neurosci 2016; 19:420-431. [PMID: 26854805 PMCID: PMC4852730 DOI: 10.1038/nn.4228] [Citation(s) in RCA: 152] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 12/22/2015] [Indexed: 12/12/2022]
Abstract
Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.
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Affiliation(s)
- Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jason L Stein
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
- Neurogenetics Program, Department of Neurology, UCLA School of Medicine, Los Angeles, USA
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, CCM, Berlin, Germany
| | - Verneri Anttila
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Kimm J E van Hulzen
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Alejandro Arias-Vasquez
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jordan W Smoller
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Thomas E Nichols
- FMRIB Centre, University of Oxford, United Kingdom
- Department of Statistics & WMG, University of Warwick, Coventry, United Kingdom
| | - Michael C Neale
- Departments of Psychiatry & Human Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Phil Lee
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Francis J McMahon
- Intramural Research Program, National Institutes of Health, US Dept of Health & Human Services, Bethesda, USA
| | - Andreas Meyer-Lindenberg
- Central Institute of Mental Health, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
| | - Manuel Mattheisen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark
- Center for integrated Sequencing, iSEQ, Aarhus University, Aarhus, Denmark
| | - Ole A Andreassen
- NORMENT - KG Jebsen Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oliver Gruber
- Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center, Goettingen, Germany
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales (UNSW), Sydney, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, Australia
| | - Roberto Roiz-Santiañez
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
- Cibersam (Centro Investigación Biomédica en Red Salud Mental), Madrid, Spain
| | - Andrew J Saykin
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, USA
- Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
| | - Stefan Ehrlich
- Department of Child and Adolescent Psychiatry, Faculty of Medicine and University Hospital, TU Dresden, Dresden, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales (UNSW), Sydney, Australia
| | - Jessica A Turner
- Georgia State University, Atlanta, USA
- Mind Research Network, Albuquerque, NM, USA
| | - Emanuel Schwarz
- Central Institute of Mental Health, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales (UNSW), Sydney, Australia
| | - Yin Yao Shugart
- Intramural Research Program, National Institutes of Health, US Dept of Health & Human Services, Bethesda, USA
| | - Yvonne YW Ho
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | - Margaret J Wright
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Psychology, University of Queensland, Brisbane, Australia
| | | | | | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- National Centre for Mental Health, Cardiff University, Cardiff, UK
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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45
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Alcohol and the Human Brain: a Systematic Review of Recent Functional Neuroimaging and Imaging Genetics Findings. CURRENT ADDICTION REPORTS 2016. [DOI: 10.1007/s40429-016-0082-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Rahmim A, Salimpour Y, Jain S, Blinder SAL, Klyuzhin IS, Smith GS, Mari Z, Sossi V. Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments. NEUROIMAGE-CLINICAL 2016; 12:e1-e9. [PMID: 27995072 PMCID: PMC5153560 DOI: 10.1016/j.nicl.2016.02.012] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 02/18/2016] [Accepted: 02/19/2016] [Indexed: 12/24/2022]
Abstract
Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = − 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = − 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression. Aim to enable image-based tracking of progression in Parkinson's disease Texture analysis of clinical dopamine transporter (DAT) SPECT images (DaTscans) Significant correlations with clinical, motor and cognitive outcomes
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Affiliation(s)
- Arman Rahmim
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Yousef Salimpour
- Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States
| | - Saurabh Jain
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Stephan A L Blinder
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
| | - Ivan S Klyuzhin
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada
| | - Gwenn S Smith
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Zoltan Mari
- Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States
| | - Vesna Sossi
- Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada
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47
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Patrick CJ, Hajcak G. Reshaping clinical science: Introduction to the Special Issue onPsychophysiology and the NIMH Research Domain Criteria(RDoC)initiative. Psychophysiology 2016; 53:281-5. [DOI: 10.1111/psyp.12613] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Revised: 12/21/2015] [Accepted: 12/21/2015] [Indexed: 01/16/2023]
Affiliation(s)
| | - Greg Hajcak
- Department of Psychology; Stony Brook University; Stony Brook New York USA
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48
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Sandoval H, Soares JC, Mwangi B, Asonye S, Alvarado LA, Zavala J, Ramirez ME, Sanches M, Enge LR, Escamilla MA. Confirmation of MRI anatomical measurements as endophenotypic markers for bipolar disorder in a new sample from the NIMH Genetics of Bipolar Disorder in Latino Populations study. Psychiatry Res Neuroimaging 2016; 247:34-41. [PMID: 26670713 DOI: 10.1016/j.pscychresns.2015.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Revised: 09/02/2015] [Accepted: 11/17/2015] [Indexed: 01/01/2023]
Abstract
The main objective of this study is to establish potential neuromorphometric differences which might act as markers of genetic risk for bipolar disorder and therefore serve as endophenotypes for discovery of genes that contribute to bipolar disorder. Magnetic resonance imaging (MRI) was used to assess structural brain volumes of 49 subjects. Volumetric analyses were first performed to test possible differences in the volume of brain structures between subjects with bipolar disorder type I (BPI) and control subjects in a new sample, based on regions previously reported in the literature as being either increased or decreased in size in bipolar patients. Subsequently, for those brain regions showing statistical difference between subjects with BPI and control subjects in our new sample, we tested whether unaffected first degree relatives (UFRs) of the BPI subjects also showed similar differences compared with controls. Four specific regions (right prefrontal, right middle prefrontal, right globus pallidus and left globus pallidus) met criteria for being possible endophenotypes for BPI in this sample.
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Affiliation(s)
- Hugo Sandoval
- Center of Excellence for Neurosciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
| | - Jair C Soares
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Benson Mwangi
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Stephanie Asonye
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Luis A Alvarado
- Division of Biostatistics and Epidemiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA
| | - Juan Zavala
- Center of Excellence for Neurosciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA; Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University, Health Sciences Center, El Paso, TX, USA
| | - Mercedes E Ramirez
- Center of Excellence for Neurosciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA; Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University, Health Sciences Center, El Paso, TX, USA
| | - Marsal Sanches
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Luke R Enge
- Department of Psychology, Social Cognitive and Neurosciences, University of Texas at El Paso, El Paso, TX, USA
| | - Michael A Escamilla
- Center of Excellence for Neurosciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA; Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University, Health Sciences Center, El Paso, TX, USA.
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49
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Won E, Ham BJ. Imaging genetics studies on monoaminergic genes in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2016; 64:311-9. [PMID: 25828849 DOI: 10.1016/j.pnpbp.2015.03.014] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 03/17/2015] [Accepted: 03/20/2015] [Indexed: 12/28/2022]
Abstract
Although depression is the leading cause of disability worldwide, current understanding of the neurobiology of depression has failed to be translated into clinical practice. Major depressive disorder (MDD) pathogenesis is considered to be significantly influenced by multiple risk genes, however genetic effects are not simply expressed at a behavioral level. Therefore the concept of endophenotype has been applied in psychiatric genetics. Imaging genetics applies anatomical or functional imaging technologies as phenotypic assays to evaluate genetic variation and their impact on behavior. This paper attempts to provide a comprehensive review of available imaging genetics studies, including reports on genetic variants that have most frequently been linked to MDD, such as the monoaminergic genes (serotonin transporter gene, monoamine oxidase A gene, tryptophan hydroxylase-2 gene, serotonin receptor 1A gene and catechol-O-methyl transferase gene), with regard to key structures involved in emotion processing, such as the hippocampus, amygdala, anterior cingulate cortex and orbitofrontal cortex.
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Affiliation(s)
- Eunsoo Won
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
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50
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Bogdan R, Pagliaccio D, Baranger DAA, Hariri AR. Genetic Moderation of Stress Effects on Corticolimbic Circuitry. Neuropsychopharmacology 2016; 41:275-96. [PMID: 26189450 PMCID: PMC4677127 DOI: 10.1038/npp.2015.216] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Revised: 07/09/2015] [Accepted: 07/11/2015] [Indexed: 02/06/2023]
Abstract
Stress exposure is associated with individual differences in corticolimbic structure and function that often mirror patterns observed in psychopathology. Gene x environment interaction research suggests that genetic variation moderates the impact of stress on risk for psychopathology. On the basis of these findings, imaging genetics, which attempts to link variability in DNA sequence and structure to neural phenotypes, has begun to incorporate measures of the environment. This research paradigm, known as imaging gene x environment interaction (iGxE), is beginning to contribute to our understanding of the neural mechanisms through which genetic variation and stress increase psychopathology risk. Although awaiting replication, evidence suggests that genetic variation within the canonical neuroendocrine stress hormone system, the hypothalamic-pituitary-adrenal axis, contributes to variability in stress-related corticolimbic structure and function, which, in turn, confers risk for psychopathology. For iGxE research to reach its full potential it will have to address many challenges, of which we discuss: (i) small effects, (ii) measuring the environment and neural phenotypes, (iii) the absence of detailed mechanisms, and (iv) incorporating development. By actively addressing these challenges, iGxE research is poised to help identify the neural mechanisms underlying genetic and environmental associations with psychopathology.
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Affiliation(s)
- Ryan Bogdan
- Department of Psychology, BRAIN Lab, Washington University in St Louis, St Louis, MO, USA
- Neurosciences Program, Division of Biology and Biomedical Sciences, Washington University in St Louis, St Louis, MO, USA
| | - David Pagliaccio
- Neurosciences Program, Division of Biology and Biomedical Sciences, Washington University in St Louis, St Louis, MO, USA
| | - David AA Baranger
- Department of Psychology, BRAIN Lab, Washington University in St Louis, St Louis, MO, USA
- Neurosciences Program, Division of Biology and Biomedical Sciences, Washington University in St Louis, St Louis, MO, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Laboratory of NeuroGenetics, Duke University, Durham, NC, USA
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