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Supekar K, de Los Angeles C, Ryali S, Kushan L, Schleifer C, Repetto G, Crossley NA, Simon T, Bearden CE, Menon V. Robust and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study. Mol Psychiatry 2024:10.1038/s41380-024-02495-8. [PMID: 38605171 DOI: 10.1038/s41380-024-02495-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 04/13/2024]
Abstract
A major genetic risk factor for psychosis is 22q11.2 deletion (22q11.2DS). However, robust and replicable functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis remain elusive due to small sample sizes and a focus on small single-site cohorts. Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date. We obtained multi-cohort clinical phenotypic and task-free fMRI data from 856 participants (101 22q11.2DS, 120 idiopathic early psychosis, 101 idiopathic autism, 123 idiopathic ADHD, and 411 healthy controls) in a case-control design. A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis. Next, stDNN was used to test the hypothesis that the functional brain signatures of 22q11.2DS-associated psychosis overlap with idiopathic early psychosis but not with autism and ADHD. stDNN-derived brain signatures distinguished 22q11.2DS from controls, and 22q11.2DS-associated psychosis with very high accuracies (86-94%) in the primary cohort and two fully independent cohorts without additional training. Robust distinguishing features of 22q11.2DS-associated psychosis emerged in the anterior insula node of the salience network and the striatum node of the dopaminergic reward pathway. These features also distinguished individuals with idiopathic early psychosis from controls, but not idiopathic autism or ADHD. Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls. Additionally, the brain signatures of 22q11.2DS-associated psychosis overlap with those of idiopathic early psychosis in the salience network and dopaminergic reward pathway, providing substantial empirical support for the theoretical aberrant salience-based model of psychosis. Collectively, our findings, replicated across multiple independent cohorts, advance the understanding of 22q11.2DS and associated psychosis, underscoring the value of 22q11.2DS as a genetic model for probing the neurobiological underpinnings of psychosis and its progression.
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Affiliation(s)
- Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Carlo de Los Angeles
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Srikanth Ryali
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Leila Kushan
- Department of Psychiatry and Behavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Charlie Schleifer
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gabriela Repetto
- Center for Genetics and Genomics, Facultad de Medicina, Clinica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Nicolas A Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Tony Simon
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, USA
- MIND Institute, University of California, Davis, Sacramento, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Behavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
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Bledsoe X, Gamazon ER. A Transcriptomic Atlas of the Human Brain Reveals Genetically Determined Aspects of Neuropsychiatric Health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.10.23287072. [PMID: 36993467 PMCID: PMC10055455 DOI: 10.1101/2023.03.10.23287072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Imaging features associated with neuropsychiatric traits can provide valuable insights into underlying pathophysiology. Using data from the UK biobank, we perform tissue-specific TWAS on over 3,500 neuroimaging phenotypes to generate a publicly accessible resource detailing the neurophysiologic consequences of gene expression. As a comprehensive catalog of neuroendophenotypes, this resource represents a powerful neurologic gene prioritization schema that can improve our understanding of brain function, development, and disease. We show that our approach generates reproducible results in internal and external replication datasets. Notably, genetically determined expression alone is shown here to enable high-fidelity reconstruction of brain structure and organization. We demonstrate complementary benefits of cross-tissue and single-tissue analyses towards an integrated neurobiology and provide evidence that gene expression outside the central nervous system provides unique insights into brain health. As an application, we show that over 40% of genes previously associated with schizophrenia in the largest GWAS meta-analysis causally affect neuroimaging phenotypes noted to be altered in schizophrenic patients.
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Affiliation(s)
- Xavier Bledsoe
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
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Jirakran K, Vasupanrajit A, Tunvirachaisakul C, Maes M. The effects of adverse childhood experiences on depression and suicidal behaviors are partially mediated by neuroticism: A subclinical manifestation of major depression. Front Psychiatry 2023; 14:1158036. [PMID: 37181874 PMCID: PMC10169750 DOI: 10.3389/fpsyt.2023.1158036] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/29/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction Neuroticism, a personality trait, can predict major depressive disorder (MDD). The current study aims to determine whether a) neuroticism is a feature of the acute state of MDD, including suicidal behaviors (SB); and b) adverse childhood experiences (ACEs) are associated with neuroticism in MDD. Methods This study included 133 participants, 67 healthy controls and 66 MDD patients, and assessed the Big 5 Inventory (BFI), ACEs using the ACE Questionnaire, and the phenome of depression using the Hamilton Depression Rating Scale (HAM-D), Beck Depression Inventory (BDI), The State-Trait Anxiety Inventory (STAI) and Columbia Suicide Severity Rating Scale (C-SSRS) scores to assess current SB. Results Neuroticism was significantly higher in MDD than controls, and it explained 64.9% of the variance in the depression phenome (a latent vector extracted from HAM-D, BDI, STAI, and current SB scores). The other BFI domains had much less (extraversion, agreeableness) or no effect (openness, conscientiousness). One latent vector could be extracted from the phenome, lifetime dysthymia, lifetime anxiety disorders and neuroticism scores. Neglect (physical and emotional) and abuse (physical, neglect and sexual) account for approximately 30% of the variance in this latent vector. Partial Least Squares analysis showed that the effects of neglect on the phenome were partially mediated by neuroticism, whereas the effects of abuse were completely mediated by neuroticism. Discussion Neuroticism (trait) and the MDD phenome (state) are both manifestations of the same latent core, with neuroticism being a subclinical manifestation of MDD.
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Affiliation(s)
- Ketsupar Jirakran
- PhD Programme in Mental Health, Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence for Maximizing Children's Developmental Potential, Department of Pediatric, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Asara Vasupanrajit
- PhD Programme in Mental Health, Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chavit Tunvirachaisakul
- King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Cognitive Impairment and Dementia Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Michael Maes
- King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Cognitive Impairment and Dementia Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- IMPACT Strategic Research Center, Barwon Health, Geelong, VIC, Australia
- Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria
- Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria
- Kyung Hee University, Seoul, Republic of Korea
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Li X, Li M. The application of zebrafish patient-derived xenograft tumor models in the development of antitumor agents. Med Res Rev 2023; 43:212-236. [PMID: 36029178 DOI: 10.1002/med.21924] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 03/09/2022] [Accepted: 07/28/2022] [Indexed: 02/04/2023]
Abstract
The cost of antitumor drug development is enormous, yet the clinical outcomes are less than satisfactory. Therefore, it is of great importance to develop effective drug screening methods that enable accurate, rapid, and high-throughput discovery of lead compounds in the process of preclinical antitumor drug research. An effective solution is to use the patient-derived xenograft (PDX) tumor animal models, which are applicable for the elucidation of tumor pathogenesis and the preclinical testing of novel antitumor compounds. As a promising screening model organism, zebrafish has been widely applied in the construction of the PDX tumor model and the discovery of antineoplastic agents. Herein, we systematically survey the recent cutting-edge advances in zebrafish PDX models (zPDX) for studies of pathogenesis mechanisms and drug screening. In addition, the techniques used in the construction of zPDX are summarized. The advantages and limitations of the zPDX are also discussed in detail. Finally, the prospects of zPDX in drug discovery, translational medicine, and clinical precision medicine treatment are well presented.
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Affiliation(s)
- Xiang Li
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Minyong Li
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
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Li X, Xu X, Chen M, Xu M, Wang W, Liu C, Yu L, Liu W, Yang W. The field phenotyping platform's next darling: Dicotyledons. FRONTIERS IN PLANT SCIENCE 2022; 13:935748. [PMID: 36092402 PMCID: PMC9449727 DOI: 10.3389/fpls.2022.935748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.
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Affiliation(s)
- Xiuni Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Xiangyao Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Menggen Chen
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Mei Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyan Wang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Chunyan Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Liang Yu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Weiguo Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyu Yang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
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Whalley L. The Cognitive Costs of Social Isolation. Neurology 2022; 99:47-48. [PMID: 35676092 DOI: 10.1212/wnl.0000000000200813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Lawrence Whalley
- From the University of Aberdeen, Institute of Applied Health Sciences.
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7
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Fan X, Zhou R, Tjahjadi T, Das Choudhury S, Ye Q. A Segmentation-Guided Deep Learning Framework for Leaf Counting. FRONTIERS IN PLANT SCIENCE 2022; 13:844522. [PMID: 35665165 PMCID: PMC9161279 DOI: 10.3389/fpls.2022.844522] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
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Affiliation(s)
- Xijian Fan
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Rui Zhou
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Tardi Tjahjadi
- School of Engineering, University of Warwick, Coventry, United Kingdom
| | - Sruti Das Choudhury
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Qiaolin Ye
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
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Schröder R, Reuter M, Faßbender K, Plieger T, Poulsen J, Lui SSY, Chan RCK, Ettinger U. The role of the SLC6A3 3' UTR VNTR in nicotine effects on cognitive, affective, and motor function. Psychopharmacology (Berl) 2022; 239:489-507. [PMID: 34854936 PMCID: PMC8638222 DOI: 10.1007/s00213-021-06028-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/11/2021] [Indexed: 01/21/2023]
Abstract
RATIONALE Nicotine has been widely studied for its pro-dopaminergic effects. However, at the behavioural level, past investigations have yielded heterogeneous results concerning effects on cognitive, affective, and motor outcomes, possibly linked to individual differences at the level of genetics. A candidate polymorphism is the 40-base-pair variable number of tandem repeats polymorphism (rs28363170) in the SLC6A3 gene coding for the dopamine transporter (DAT). The polymorphism has been associated with striatal DAT availability (9R-carriers > 10R-homozygotes), and 9R-carriers have been shown to react more strongly to dopamine agonistic pharmacological challenges than 10R-homozygotes. OBJECTIVES In this preregistered study, we hypothesized that 9R-carriers would be more responsive to nicotine due to genotype-related differences in DAT availability and resulting dopamine activity. METHODS N=194 non-smokers were grouped according to their genotype (9R-carriers, 10R-homozygotes) and received either 2-mg nicotine or placebo gum in a between-subject design. Spontaneous blink rate (SBR) was obtained as an indirect measure of striatal dopamine activity and smooth pursuit, stop signal, simple choice and affective processing tasks were carried out in randomized order. RESULTS Reaction times were decreased under nicotine compared to placebo in the simple choice and stop signal tasks, but nicotine and genotype had no effects on any of the other task outcomes. Conditional process analyses testing the mediating effect of SBR on performance and how this is affected by genotype yielded no significant results. CONCLUSIONS Overall, we could not confirm our main hypothesis. Individual differences in nicotine response could not be explained by rs28363170 genotype.
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Affiliation(s)
| | - Martin Reuter
- Department of Psychology, University of Bonn, Bonn, Germany
| | - Kaja Faßbender
- Department of Psychology, University of Bonn, Bonn, Germany
| | - Thomas Plieger
- Department of Psychology, University of Bonn, Bonn, Germany
| | - Jessie Poulsen
- Nicotine Science Center, Fertin Pharma A/S, Vejle, Denmark
| | - Simon S Y Lui
- Department of Psychiatry, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience (NACN) Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Ulrich Ettinger
- Department of Psychology, University of Bonn, Bonn, Germany.
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9
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PlantNet: transfer learning-based fine-grained network for high-throughput plants recognition. Soft comput 2022. [DOI: 10.1007/s00500-021-06689-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Henson RN, Suri S, Knights E, Rowe JB, Kievit RA, Lyall DM, Chan D, Eising E, Fisher SE. Effect of apolipoprotein E polymorphism on cognition and brain in the Cambridge Centre for Ageing and Neuroscience cohort. Brain Neurosci Adv 2020; 4:2398212820961704. [PMID: 33088920 PMCID: PMC7545750 DOI: 10.1177/2398212820961704] [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] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 08/27/2020] [Indexed: 01/01/2023] Open
Abstract
Polymorphisms in the apolipoprotein E (APOE) gene have been associated with individual differences in cognition, brain structure and brain function. For example, the ε4 allele has been associated with cognitive and brain impairment in old age and increased risk of dementia, while the ε2 allele has been claimed to be neuroprotective. According to the ‘antagonistic pleiotropy’ hypothesis, these polymorphisms have different effects across the lifespan, with ε4, for example, postulated to confer benefits on cognitive and brain functions earlier in life. In this stage 2 of the Registered Report – https://osf.io/bufc4, we report the results from the cognitive and brain measures in the Cambridge Centre for Ageing and Neuroscience cohort (www.cam-can.org). We investigated the antagonistic pleiotropy hypothesis by testing for allele-by-age interactions in approximately 600 people across the adult lifespan (18–88 years), on six outcome variables related to cognition, brain structure and brain function (namely, fluid intelligence, verbal memory, hippocampal grey-matter volume, mean diffusion within white matter and resting-state connectivity measured by both functional magnetic resonance imaging and magnetoencephalography). We found no evidence to support the antagonistic pleiotropy hypothesis. Indeed, Bayes factors supported the null hypothesis in all cases, except for the (linear) interaction between age and possession of the ε4 allele on fluid intelligence, for which the evidence for faster decline in older ages was ambiguous. Overall, these pre-registered analyses question the antagonistic pleiotropy of APOE polymorphisms, at least in healthy adults.
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Affiliation(s)
- Richard N Henson
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sana Suri
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Ethan Knights
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - James B Rowe
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Rogier A Kievit
- MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Donald M Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Dennis Chan
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Else Eising
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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11
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Deng H, Xu T, Zhou Y, Miao T. Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System. SENSORS (BASEL, SWITZERLAND) 2020; 20:E812. [PMID: 32028625 PMCID: PMC7038701 DOI: 10.3390/s20030812] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/31/2020] [Accepted: 02/01/2020] [Indexed: 12/17/2022]
Abstract
Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.
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Affiliation(s)
- Hanbing Deng
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; (H.D.); (Y.Z.); (T.M.)
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China
| | - Tongyu Xu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; (H.D.); (Y.Z.); (T.M.)
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China
| | - Yuncheng Zhou
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; (H.D.); (Y.Z.); (T.M.)
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China
| | - Teng Miao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; (H.D.); (Y.Z.); (T.M.)
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang 110866, China
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12
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Larsen KM, Dzafic I, Siebner HR, Garrido MI. Alteration of functional brain architecture in 22q11.2 deletion syndrome – Insights into susceptibility for psychosis. Neuroimage 2019; 190:154-171. [DOI: 10.1016/j.neuroimage.2018.09.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 08/30/2018] [Accepted: 09/02/2018] [Indexed: 12/23/2022] Open
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13
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Wintermark M, Colen R, Whitlow CT, Zaharchuk G. The vast potential and bright future of neuroimaging. Br J Radiol 2018; 91:20170505. [PMID: 29848016 DOI: 10.1259/bjr.20170505] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Significant advances in anatomical and functional neuroimaging techniques have allowed researchers and clinicians to visualize the brain in action. The field of neuroimaging currently includes newer and faster scanners, improved image quality, higher spatial and temporal resolution and diverse methods of acquisition and analysis. Beyond simply imaging brain structures, these developments enable quantitative assessment of the microstructural and functional architecture, perfusion and metabolism of the brain. The resultant highly granular data have the potential to greatly improve characterization of neurological, neurosurgical and psychiatric disorders without invasive neurosurgery. However, the surge in neuroimaging data that can be collected over a relatively short acquisition period has led to a "big data" problem, where novel methods are needed to appropriately extract and analyze information and integrate data with other types of big data, such as genomic and proteomic data. Another challenge is the translation of these new technologies from basic science into clinical practice, so that they can be leveraged to improve patient outcomes and alleviate human disease. Critical to this endeavor is research comparing the effectiveness and outcomes of these advancements to allow widespread acceptance in the modern, economically constrained healthcare system. This review aims to illustrate the different facets of cutting edge neuroimaging techniques, as well as the potential role of these methods as clinical tools for evaluating the breadth of diseases that affect the brain.
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Affiliation(s)
- Max Wintermark
- 1 Department of Radiology, Division of Neuroradiology, Stanford University , Palo Alto, CA , USA
| | - Rivka Colen
- 2 Department of Diagnostic Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center , Houston, TX , USA
| | - Christopher T Whitlow
- 3 Departments of Radiology and Biomedical Engineering and Clinical Translational Science Institute, Wake Forest School of Medicine , Winston-Salem, NC , USA
| | - Greg Zaharchuk
- 1 Department of Radiology, Division of Neuroradiology, Stanford University , Palo Alto, CA , USA
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Viviani R, Lehmann ML, Stingl JC. Use of magnetic resonance imaging in pharmacogenomics. Br J Clin Pharmacol 2014; 77:684-94. [PMID: 23802603 PMCID: PMC3971984 DOI: 10.1111/bcp.12197] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 06/18/2013] [Indexed: 01/11/2023] Open
Abstract
Because of the large variation in the response to psychoactive medication, many studies have attempted to uncover genetic factors that determine response. While considerable knowledge exists on the large effects of genetic polymorphisms on pharmacokinetics and plasma concentrations of drugs, effects of the concentration at the target site and pharmacodynamic effects on brain functions in disease are much less known. This article reviews the role of magnetic resonance imaging (MRI) to visualize response to medication in brain behaviour circuits in vivo in humans and assess the influence of pharmacogenetic factors. Two types of studies have been used to characterize effects of medication and genetic variation. In task-related activation studies the focus is on changes in the activity of a neural circuit associated with a specific psychological process. The second type of study investigates resting state perfusion. These studies provide an assessment of vascular changes associated with bioavailability of drugs in the brain, but may also assess changes in neural activity after binding of centrally active agents. Task-related pharmacogenetic studies of cognitive function have characterized the effects in the prefrontal cortex of genetic polymorphisms of dopamine receptors (DRD2), metabolic enzymes (COMT) and in the post-synaptic signalling cascade under the administration of dopamine agonists and antagonists. In contrast, pharmacogenetic imaging with resting state perfusion is still in its infancy. However, the quantitative nature of perfusion imaging, its non-invasive character and its repeatability might be crucial assets in visualizing the effects of medication in vivo in man during therapy.
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Affiliation(s)
- Roberto Viviani
- Department of Psychiatry and Psychotherapy III, University of Ulm, Ulm, Germany; Institute of Psychology, University of Innsbruck, Innsbruck, Austria
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Damiano CR, Aloi J, Dunlap K, Burrus CJ, Mosner MG, Kozink RV, McLaurin RE, Mullette-Gillman OA, Carter RM, Huettel SA, McClernon FJ, Ashley-Koch A, Dichter GS. Association between the oxytocin receptor (OXTR) gene and mesolimbic responses to rewards. Mol Autism 2014; 5:7. [PMID: 24485285 PMCID: PMC3922109 DOI: 10.1186/2040-2392-5-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Accepted: 01/17/2014] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND There has been significant progress in identifying genes that confer risk for autism spectrum disorders (ASDs). However, the heterogeneity of symptom presentation in ASDs impedes the detection of ASD risk genes. One approach to understanding genetic influences on ASD symptom expression is to evaluate relations between variants of ASD candidate genes and neural endophenotypes in unaffected samples. Allelic variations in the oxytocin receptor (OXTR) gene confer small but significant risk for ASDs for which the underlying mechanisms may involve associations between variability in oxytocin signaling pathways and neural response to rewards. The purpose of this preliminary study was to investigate the influence of allelic variability in the OXTR gene on neural responses to monetary rewards in healthy adults using functional magnetic resonance imaging (fMRI). METHODS The moderating effects of three single nucleotide polymorphisms (SNPs) (rs1042778, rs2268493 and rs237887) of the OXTR gene on mesolimbic responses to rewards were evaluated using a monetary incentive delay fMRI task. RESULTS T homozygotes of the rs2268493 SNP demonstrated relatively decreased activation in mesolimbic reward circuitry (including the nucleus accumbens, amygdala, insula, thalamus and prefrontal cortical regions) during the anticipation of rewards but not during the outcome phase of the task. Allelic variation of the rs1042778 and rs237887 SNPs did not moderate mesolimbic activation during either reward anticipation or outcomes. CONCLUSIONS This preliminary study suggests that the OXTR SNP rs2268493, which has been previously identified as an ASD risk gene, moderates mesolimbic responses during reward anticipation. Given previous findings of decreased mesolimbic activation during reward anticipation in ASD, the present results suggest that OXTR may confer ASD risk via influences on the neural systems that support reward anticipation.
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Affiliation(s)
- Cara R Damiano
- Department of Psychology, University of North Carolina, CB#3270, Davie Hall, UNC-CH, Chapel Hill, NC 27599, USA.
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Wöstmann NM, Aichert DS, Costa A, Rubia K, Möller HJ, Ettinger U. Reliability and plasticity of response inhibition and interference control. Brain Cogn 2013; 81:82-94. [DOI: 10.1016/j.bandc.2012.09.010] [Citation(s) in RCA: 139] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2011] [Revised: 08/27/2012] [Accepted: 09/20/2012] [Indexed: 11/15/2022]
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Brüggemann N, Vegt J, Klein C, Siebner H. Neurobildgebung genetischer Aspekte der Parkinson-Krankheit. DER NERVENARZT 2010; 81:1196-203. [DOI: 10.1007/s00115-010-3024-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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