251
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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: 32] [Impact Index Per Article: 3.6] [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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252
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Chen B, Xu T, Zhou C, Wang L, Yang N, Wang Z, Dong HM, Yang Z, Zang YF, Zuo XN, Weng XC. Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month. PLoS One 2015; 10:e0144963. [PMID: 26714192 PMCID: PMC4694646 DOI: 10.1371/journal.pone.0144963] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 11/27/2015] [Indexed: 11/18/2022] Open
Abstract
Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.
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Affiliation(s)
- Bing Chen
- Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Ting Xu
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Changle Zhou
- Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China
| | - Luoyu Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Ning Yang
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ze Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Hao-Ming Dong
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhi Yang
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu-Feng Zang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Faculty of Psychology, Southwest University, Beibei, Chongqing 400715, China
- Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi 530001, China
| | - Xu-Chu Weng
- Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
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253
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He Y, Xu T, Zhang W, Zuo XN. Lifespan anxiety is reflected in human amygdala cortical connectivity. Hum Brain Mapp 2015; 37:1178-93. [PMID: 26859312 PMCID: PMC5064618 DOI: 10.1002/hbm.23094] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 12/05/2015] [Accepted: 12/08/2015] [Indexed: 01/05/2023] Open
Abstract
The amygdala plays a pivotal role in processing anxiety and connects to large‐scale brain networks. However, intrinsic functional connectivity (iFC) between amygdala and these networks has rarely been examined in relation to anxiety, especially across the lifespan. We employed resting‐state functional MRI data from 280 healthy adults (18–83.5 yrs) to elucidate the relationship between anxiety and amygdala iFC with common cortical networks including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default network. Global and network‐specific iFC were separately computed as mean iFC of amygdala with the entire cerebral cortex and each cortical network. We detected negative correlation between global positive amygdala iFC and trait anxiety. Network‐specific associations between amygdala iFC and anxiety were also detectable. Specifically, the higher iFC strength between the left amygdala and the limbic network predicted lower state anxiety. For the trait anxiety, left amygdala anxiety–connectivity correlation was observed in both somatomotor and dorsal attention networks, whereas the right amygdala anxiety–connectivity correlation was primarily distributed in the frontoparietal and ventral attention networks. Ventral attention network exhibited significant anxiety–gender interactions on its iFC with amygdala. Together with findings from additional vertex‐wise analysis, these data clearly indicated that both low‐level sensory networks and high‐level associative networks could contribute to detectable predictions of anxiety behaviors by their iFC profiles with the amygdala. This set of systems neuroscience findings could lead to novel functional network models on neural correlates of human anxiety and provide targets for novel treatment strategies on anxiety disorders. Hum Brain Mapp 37:1178–1193, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Ye He
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ting Xu
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Wei Zhang
- Department of Rehabilitation Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Faculty of Psychology, Southwest University, Chongqing, Beibei, 400715, China.,Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi, 530001, China
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254
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Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states. Neuroimage 2015. [DOI: 10.1016/j.neuroimage.2015.07.044] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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255
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A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults. Sci Data 2015; 2:150056. [PMID: 26528395 PMCID: PMC4623457 DOI: 10.1038/sdata.2015.56] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 09/30/2015] [Indexed: 12/16/2022] Open
Abstract
Recently, magnetic resonance imaging (MRI) has been widely used to investigate the structures and functions of the human brain in health and disease in vivo. However, there are growing concerns about the test-retest reliability of structural and functional measurements derived from MRI data. Here, we present a test-retest dataset of multi-modal MRI including structural MRI (S-MRI), diffusion MRI (D-MRI) and resting-state functional MRI (R-fMRI). Fifty-seven healthy young adults (age range: 19-30 years) were recruited and completed two multi-modal MRI scan sessions at an interval of approximately 6 weeks. Each scan session included R-fMRI, S-MRI and D-MRI data. Additionally, there were two separated R-fMRI scans at the beginning and at the end of the first session (approximately 20 min apart). This multi-modal MRI dataset not only provides excellent opportunities to investigate the short- and long-term test-retest reliability of the brain's structural and functional measurements at the regional, connectional and network levels, but also allows probing the test-retest reliability of structural-functional couplings in the human brain.
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256
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Wang XH, Li L, Xu T, Ding Z. Investigating the Temporal Patterns within and between Intrinsic Connectivity Networks under Eyes-Open and Eyes-Closed Resting States: A Dynamical Functional Connectivity Study Based on Phase Synchronization. PLoS One 2015; 10:e0140300. [PMID: 26469182 PMCID: PMC4607488 DOI: 10.1371/journal.pone.0140300] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 09/23/2015] [Indexed: 01/19/2023] Open
Abstract
The brain active patterns were organized differently under resting states of eyes open (EO) and eyes closed (EC). The altered voxel-wise and regional-wise resting state active patterns under EO/EC were found by static analysis. More importantly, dynamical spontaneous functional connectivity has been observed in the resting brain. To the best of our knowledge, the dynamical mechanisms of intrinsic connectivity networks (ICNs) under EO/EC remain largely unexplored. The goals of this paper were twofold: 1) investigating the dynamical intra-ICN and inter-ICN temporal patterns during resting state; 2) analyzing the altered dynamical temporal patterns of ICNs under EO/EC. To this end, a cohort of healthy subjects with scan conditions of EO/EC were recruited from 1000 Functional Connectomes Project. Through Hilbert transform, time-varying phase synchronization (PS) was applied to evaluate the inter-ICN synchrony. Meanwhile, time-varying amplitude was analyzed as dynamical intra-ICN temporal patterns. The results found six micro-states of inter-ICN synchrony. The medial visual network (MVN) showed decreased intra-ICN amplitude during EC relative to EO. The sensory-motor network (SMN) and auditory network (AN) exhibited enhanced intra-ICN amplitude during EC relative to EO. Altered inter-ICN PS was found between certain ICNs. Particularly, the SMN and AN exhibited enhanced PS to other ICNs during EC relative to EO. In addition, the intra-ICN amplitude might influence the inter-ICN synchrony. Moreover, default mode network (DMN) might play an important role in information processing during EO/EC. Together, the dynamical temporal patterns within and between ICNs were altered during different scan conditions of EO/EC. Overall, the dynamical intra-ICN and inter-ICN temporal patterns could benefit resting state fMRI-related research, and could be potential biomarkers for human functional connectome.
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Affiliation(s)
- Xun-Heng Wang
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
- * E-mail: (XHW); (LL)
| | - Lihua Li
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
- * E-mail: (XHW); (LL)
| | - Tao Xu
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou,310014, China
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257
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Orban P, Madjar C, Savard M, Dansereau C, Tam A, Das S, Evans AC, Rosa-Neto P, Breitner JCS, Bellec P. Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer's disease. Sci Data 2015; 2:150043. [PMID: 26504522 PMCID: PMC4603392 DOI: 10.1038/sdata.2015.43] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 07/21/2015] [Indexed: 11/21/2022] Open
Abstract
We present a test-retest dataset of resting-state fMRI data obtained in 80 cognitively normal elderly volunteers enrolled in the “Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease” (PREVENT-AD) Cohort. Subjects with a family history of Alzheimer's disease in first-degree relatives were recruited as part of an on-going double blind randomized clinical trial of Naproxen or placebo. Two pairs of scans were acquired ~3 months apart, allowing the assessment of both intra- and inter-session reliability, with the possible caveat of treatment effects as a source of inter-session variation. Using the NeuroImaging Analysis Kit (NIAK), we report on the standard quality of co-registration and motion parameters of the data, and assess their validity based on the spatial distribution of seed-based connectivity maps as well as intra- and inter-session reliability metrics in the default-mode network. This resource, released publicly as sample UM1 of the Consortium for Reliability and Reproducibility (CoRR), will benefit future studies focusing on the preclinical period preceding the appearance of dementia in Alzheimer's disease.
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Affiliation(s)
- Pierre Orban
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 Queen Mary , Montreal, QC H3W 1W5, Canada ; Université de Montréal, 2900 Boulevard Edouard-Montpetit , Montreal, QC H3T 1J4, Canada
| | - Cécile Madjar
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada
| | - Mélissa Savard
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada
| | - Christian Dansereau
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 Queen Mary , Montreal, QC H3W 1W5, Canada ; Université de Montréal, 2900 Boulevard Edouard-Montpetit , Montreal, QC H3T 1J4, Canada
| | - Angela Tam
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada
| | - Samir Das
- McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada ; McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University , Montreal, QC H3A 2B4, Canada
| | - Alan C Evans
- McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada ; McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University , Montreal, QC H3A 2B4, Canada
| | - Pedro Rosa-Neto
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada ; McGill University Research Centre for Studies in Aging, 6825 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada
| | - John C S Breitner
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Douglas Mental Health University Institute Research Centre, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; McGill University, 845 Sherbrooke W , Montreal, QC H3A 0G4, Canada
| | - Pierre Bellec
- StoP-AD Centre, Centre for Studies on Prevention of Alzheimer's disease, 6875 LaSalle Boulevard , Montreal, QC H4H 1R3, Canada ; Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 Queen Mary , Montreal, QC H3W 1W5, Canada ; Université de Montréal, 2900 Boulevard Edouard-Montpetit , Montreal, QC H3T 1J4, Canada
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258
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Wang J, Yang N, Liao W, Zhang H, Yan CG, Zang YF, Zuo XN. Dorsal anterior cingulate cortex in typically developing children: Laterality analysis. Dev Cogn Neurosci 2015; 15:117-29. [PMID: 26602957 PMCID: PMC6989820 DOI: 10.1016/j.dcn.2015.10.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Revised: 10/03/2015] [Accepted: 10/05/2015] [Indexed: 12/13/2022] Open
Abstract
We aimed to elucidate the dACC laterality in typically developing children and their sex/age-related differences with a sample of 84 right-handed children (6-16 years, 42 boys). We first replicated the previous finding observed in adults that gray matter density asymmetry in the dACC was region-specific: leftward (left > right) in its superior part, rightward (left < right) in its inferior part. Intrinsic connectivity analysis of these regions further revealed region-specific asymmetric connectivity profiles in dACC as well as their sex and age differences. Specifically, the superior dACC connectivity with frontoparietal network and the inferior dACC connectivity with visual network are rightward. The superior dACC connectivity with the default network (lateral temporal cortex) was more involved in the left hemisphere. In contrast, the inferior dACC connectivity with the default network (anterior medial prefrontal cortex) was more lateralized towards the right hemisphere. The superior dACC connectivity with lateral visual cortex was more distinct across two hemispheres in girls than that in boys. This connection in boys changed with age from right-prominent to left-prominent asymmetry whereas girls developed the connection from left-prominent to no asymmetry. These findings not only highlight the complexity and laterality of the dACC but also provided insights into dynamical structure-function relationships during the development.
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Affiliation(s)
- Jue Wang
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ning Yang
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Wei Liao
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Han Zhang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu-Feng Zang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Faculty of Psychology, Southwest University, Chongqing 400715, China
- Department of Psychology, School of Education Science, Guangxi Teachers Education University, Guangxi 530001, China
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259
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Wang J, Lu M, Fan Y, Wen X, Zhang R, Wang B, Ma Q, Song Z, He Y, Wang J, Huang R. Exploring brain functional plasticity in world class gymnasts: a network analysis. Brain Struct Funct 2015; 221:3503-19. [DOI: 10.1007/s00429-015-1116-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 09/16/2015] [Indexed: 12/14/2022]
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260
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Somandepalli K, Kelly C, Reiss PT, Zuo XN, Craddock RC, Yan CG, Petkova E, Castellanos FX, Milham MP, Di Martino A. Short-term test-retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder. Dev Cogn Neurosci 2015; 15:83-93. [PMID: 26365788 PMCID: PMC6989828 DOI: 10.1016/j.dcn.2015.08.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 08/07/2015] [Accepted: 08/09/2015] [Indexed: 01/05/2023] Open
Abstract
Children with or without ADHD have moderate/high R-fMRI test–retest reliability. Reliability is greater in controls than ADHD across most R-fMRI metrics. Regional differences in ICC related to diagnostic groups reflect underlying pathophysiology for ADHD affecting both inter and intra subject variability.
To date, only one study has examined test–retest reliability of resting state fMRI (R-fMRI) in children, none in clinical developing groups. Here, we assessed short-term test–retest reliability in a sample of 46 children (11–17.9 years) with attention-deficit/hyperactivity disorder (ADHD) and 57 typically developing children (TDC). Our primary test–retest reliability measure was the intraclass correlation coefficient (ICC), quantified for a range of R-fMRI metrics. We aimed to (1) survey reliability within and across diagnostic groups, and (2) compare voxel-wise ICC between groups. We found moderate-to-high ICC across all children and within groups, with higher-order functional networks showing greater ICC. Nearly all R-fMRI metrics exhibited significantly higher ICC in TDC than in children with ADHD for one or more regions. In particular, posterior cingulate and ventral precuneus exhibited group differences in ICC across multiple measures. In the context of overall moderate-to-high test–retest reliability in children, regional differences in ICC related to diagnostic groups likely reflect the underlying pathophysiology for ADHD. Our currently limited understanding of the factors contributing to inter- and intra-subject variability in ADHD underscores the need for large initiatives aimed at examining their impact on test–retest reliability in both clinical and developing populations.
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Affiliation(s)
- Krishna Somandepalli
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA
| | - Clare Kelly
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA
| | - Philip T Reiss
- Division of Biostatistics, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - R C Craddock
- Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Chao-Gan Yan
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Eva Petkova
- Division of Biostatistics, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - F X Castellanos
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Adriana Di Martino
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, The Child Study Center at NYU Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA.
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261
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Holmes AJ, Hollinshead MO, O'Keefe TM, Petrov VI, Fariello GR, Wald LL, Fischl B, Rosen BR, Mair RW, Roffman JL, Smoller JW, Buckner RL. Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci Data 2015; 2:150031. [PMID: 26175908 PMCID: PMC4493828 DOI: 10.1038/sdata.2015.31] [Citation(s) in RCA: 281] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 06/04/2015] [Indexed: 01/26/2023] Open
Abstract
The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset’s utility.
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Affiliation(s)
- Avram J Holmes
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA ; Department of Psychology, Harvard University , Cambridge, MA 02138, USA ; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School , Boston, MA 02114, USA ; Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Marisa O Hollinshead
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA ; Department of Psychology, Harvard University , Cambridge, MA 02138, USA ; Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Timothy M O'Keefe
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA
| | - Victor I Petrov
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA
| | - Gabriele R Fariello
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA ; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School , Boston, MA 02114, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Ross W Mair
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA ; Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School , Boston, MA 02114, USA ; Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School , Boston, MA 02114, USA
| | - Randy L Buckner
- Center for Brain Science, Harvard University , Cambridge, MA 02138, USA ; Department of Psychology, Harvard University , Cambridge, MA 02138, USA ; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School , Boston, MA 02114, USA ; Athinoula A. Martinos Center for Biomedical Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA 02129, USA
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262
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Bourke JH, Wall MB. phMRI: methodological considerations for mitigating potential confounding factors. Front Neurosci 2015; 9:167. [PMID: 25999812 PMCID: PMC4423340 DOI: 10.3389/fnins.2015.00167] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 04/23/2015] [Indexed: 11/16/2022] Open
Abstract
Pharmacological Magnetic Resonance Imaging (phMRI) is a variant of conventional MRI that adds pharmacological manipulations in order to study the effects of drugs, or uses pharmacological probes to investigate basic or applied (e.g., clinical) neuroscience questions. Issues that may confound the interpretation of results from various types of phMRI studies are briefly discussed, and a set of methodological strategies that can mitigate these problems are described. These include strategies that can be employed at every stage of investigation, from study design to interpretation of resulting data, and additional techniques suited for use with clinical populations are also featured. Pharmacological MRI is a challenging area of research that has both significant advantages and formidable difficulties, however with due consideration and use of these strategies many of the key obstacles can be overcome.
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Affiliation(s)
- Julius H Bourke
- Centre for Psychiatry, The London School of Medicine and Dentistry, Wolfson Barts Institute for Preventive Medicine, Queen Mary University of London London, UK
| | - Matthew B Wall
- Imanova Centre for Imaging Sciences, Imperial College London, Hammersmith Hospital London, UK ; Division of Brain Sciences, Imperial College London London, UK
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263
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Craddock RC, Tungaraza RL, Milham MP. Connectomics and new approaches for analyzing human brain functional connectivity. Gigascience 2015; 4:13. [PMID: 25810900 PMCID: PMC4373299 DOI: 10.1186/s13742-015-0045-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 01/18/2015] [Indexed: 11/10/2022] Open
Abstract
Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a “big data” problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.
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Affiliation(s)
- R Cameron Craddock
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA
| | - Rosalia L Tungaraza
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, 10962 New York USA ; Center for the Developing Brain, Child Mind Institute, 445 Park Ave, New York, 10022 New York USA
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264
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Calhoun VD. A spectrum of sharing: maximization of information content for brain imaging data. Gigascience 2015; 4:2. [PMID: 25653850 PMCID: PMC4316396 DOI: 10.1186/s13742-014-0042-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 12/17/2014] [Indexed: 12/14/2022] Open
Abstract
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a spectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges. A summary of associated challenges of and possible solutions is presented in this review and includes: 1) a discussion of tools to monitor quality of data as it is collected and encourage adoption of data mapping standards; 2) sharing of time-series data (not just summary maps or regions); and 3) the use of analytic approaches which maximize sharing potential as much as possible. Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning.
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Affiliation(s)
- Vince D Calhoun
- />The Mind Research Network & LBERI, 1101 Yale Blvd NE, Albuquerque, New Mexico 87106 USA
- />Department of ECE, University of New Mexico, Albuquerque, New Mexico USA
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265
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Affiliation(s)
- Franco Pestilli
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, Indiana University , 1101 E 10th Street, Bloomington, Indiana 47405, USA
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266
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Gorgolewski KJ, Mendes N, Wilfling D, Wladimirow E, Gauthier CJ, Bonnen T, Ruby FJM, Trampel R, Bazin PL, Cozatl R, Smallwood J, Margulies DS. A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures. Sci Data 2015; 2:140054. [PMID: 25977805 PMCID: PMC4412153 DOI: 10.1038/sdata.2014.54] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 10/29/2014] [Indexed: 01/08/2023] Open
Abstract
Here we present a test-retest dataset of functional magnetic resonance imaging (fMRI) data acquired at rest. 22 participants were scanned during two sessions spaced one week apart. Each session includes two 1.5 mm isotropic whole-brain scans and one 0.75 mm isotropic scan of the prefrontal cortex, giving a total of six time-points. Additionally, the dataset includes measures of mood, sustained attention, blood pressure, respiration, pulse, and the content of self-generated thoughts (mind wandering). This data enables the investigation of sources of both intra- and inter-session variability not only limited to physiological changes, but also including alterations in cognitive and affective states, at high spatial resolution. The dataset is accompanied by a detailed experimental protocol and source code of all stimuli used.
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Affiliation(s)
- Krzysztof J Gorgolewski
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | - Natacha Mendes
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | - Domenica Wilfling
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | - Elisabeth Wladimirow
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | - Claudine J Gauthier
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany ; Concordia University/PERFORM Center , Montreal, Canada H4B 1R6
| | - Tyler Bonnen
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | | | - Robert Trampel
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | - Pierre-Louis Bazin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | - Roberto Cozatl
- Databases and IT Group, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
| | | | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Germany
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268
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269
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Abstract
Sharing drafts of scientific manuscripts on preprint hosting services for early exposure and pre-publication feedback is a well-accepted practice in fields such as physics, astronomy, or mathematics. The field of neuroscience, however, has yet to adopt the preprint model. A reason for this reluctance might partly be the lack of central preprint services for the field of neuroscience. To address this issue, we announce the launch of Preprints of the R-fMRI Network (PRN), a community funded preprint hosting service. PRN provides free-submission and free hosting of manuscripts for resting state functional magnetic resonance imaging (R-fMRI) and neuroscience related studies. Submitted articles are openly discussed and receive feedback from readers and a panel of invited consultants from the R-fMRI Network. All manuscripts and feedback are freely accessible online with citable permanent URL for open-access. The goal of PRN is to supplement the peer reviewed journal publication system - by more rapidly communicating the latest research achievements throughout the world. We hope PRN would help the field to embrace the preprint model and thus further accelerate R-fMRI and neuroscience related studies, eventually enhancing human mental health.
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Affiliation(s)
- Chao-Gan Yan
- The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA ; Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Rd, Chaoyang District, Beijing, 100101, China ; Department of Child and Adolescent Psychiatry, New York University Langone Medical Center, New York, NY, USA ; Editorial Office of PRN, the R-fMRI Network, Inc., New York, NY, USA
| | - Qingyang Li
- Editorial Office of PRN, the R-fMRI Network, Inc., New York, NY, USA
| | - Lei Gao
- Editorial Office of PRN, the R-fMRI Network, Inc., New York, NY, USA ; Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China
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