Linked dimensions of psychopathology and connectivity in functional brain networks
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Abstract
Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology – mood, psychosis, fear, and externalizing behavior – are associated ( r = 0.68–0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset ( n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.
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Multiple psychiatric disorders are associated with alterations in functional connectivity of frontoparietal and cingulo-opercular networks at rest.
“In particular, multiple psychiatric disorders are associated with hypoactivation of the FPN and CON during CC tasks [2, 204], alterations in functional connectivity of these networks at rest [3, 205], and alterations in gray matter volume in nodes of these networks, including the dorsal ACC, insula, and dorsomedial and vmPFC [2, 3, 206].”
From The role of prefrontal cortex in cognitive control and executive function (2022) · cites this paper as [205]
The Results (‘Common and dissociable patterns of connectivity’; page unavailable) link several psychopathology dimensions to altered resting-state connectivity involving the fronto-parietal network. But the paper lists the cingulo-opercular and salience networks separately (Fig. 2), and the cited findings identify the salience network, not specifically the cingulo-opercular network. Its findings also concern symptom dimensions that cross diagnoses rather than comparisons across multiple diagnosed disorders. (LLM, conf=0.83)
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Cites
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888 · see citing works
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812 · see citing works
A multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111 · see citing works
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124 · see citing works
Toward the future of psychiatric diagnosis: the seven pillars of RDoC
Cuthbert, B. N., & Insel, T. R. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-126 · Cited by 3,053 · see citing works
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170 · see citing works
Abnormal neural oscillations and synchrony in schizophrenia
Uhlhaas, P. J., & Singer, W. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2774 · Cited by 2,120 · see citing works
Intrinsic functional architecture in the anaesthetized monkey brain
Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., Van Essen, D. C., Zempel, J. M., Snyder, L. H., Corbetta, M., & Raichle, M. E. (2007) · Nature · doi:10.1038/nature05758 · Cited by 1,786 · see citing works
Why is depression more prevalent in women?
Albert, P. R. (2015) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.150205 · Cited by 1,785 · see citing works
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734 · see citing works
Identification of a Common Neurobiological Substrate for Mental Illness
Goodkind, M., Eickhoff, S. B., Oathes, D. J., Jiang, Y., Chang, A., Jones-Hagata, L. B., Ortega, B. N., Zaiko, Y. V., Roach, E. L., Korgaonkar, M. S., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.2206 · Cited by 1,404 · see citing works
Pipkins, H. R., Bradshaw, J. L., Keller, L. E., Swiatlo, E., & McDaniel, L. S. (2017) · PLOS ONE · doi:10.1371/journal.pone.0179159 · Cited by 1,338 · see citing works
JACOBI, F., WITTCHEN, H.-U., HÖLTING, C., HÖFLER, M., PFISTER, H., MÜLLER, N., & LIEB, R. (2004) · Psychological Medicine · doi:10.1017/s0033291703001399 · Cited by 1,097 · see citing works
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., Ugurbil, K., Barch, D. M., Van Essen, D. C., & Miller, K. L. (2015) · Nature Neuroscience · doi:10.1038/nn.4125 · Cited by 859 · see citing works
Insel, T. R., & Cuthbert, B. N. (2015) · Science · doi:10.1126/science.aab2358 · Cited by 840 · see citing works
Neurodevelopmental model of schizophrenia: update 2012
Rapoport, J. L., Giedd, J. N., & Gogtay, N. (2012) · Molecular Psychiatry · doi:10.1038/mp.2012.23 · Cited by 790 · see citing works
Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers
Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14091200 · Cited by 768 · see citing works
McTeague, L. M., Huemer, J., Carreon, D. M., Jiang, Y., Eickhoff, S. B., & Etkin, A. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.16040400 · Cited by 635 · see citing works
Functional network dysfunction in anxiety and anxiety disorders
Sylvester, C. M., Corbetta, M., Raichle, M. E., Rodebaugh, T. L., Schlaggar, B. L., Sheline, Y. I., Zorumski, C. F., & Lenze, E. J. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.04.012 · Cited by 579 · see citing works
Parcellating cortical functional networks in individuals
Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4164 · Cited by 527 · see citing works
Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.064 · Cited by 504 · see citing works
Self-citationThe Frontoparietal Control System: A Central Role in Mental Health
Cole, M. W., Repovš, G., & Anticevic, A. (2014) · The Neuroscientist · doi:10.1177/1073858414525995 · Cited by 461 · see citing works
A Neurodevelopmental Perspective on the Research Domain Criteria (RDoC) Framework
Casey, B. J., Oliveri, M. E., & Insel, T. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.01.006 · Cited by 369 · see citing works
Emergence of system roles in normative neurodevelopment
Gu, S., Satterthwaite, T. D., Medaglia, J. D., Yang, M., Gur, R. E., Gur, R. C., & Bassett, D. S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1502829112 · Cited by 338 · see citing works
Self-citationGennatas, E. D., Avants, B. B., Wolf, D. H., Satterthwaite, T. D., Ruparel, K., Ciric, R., Hakonarson, H., Gur, R. E., & Gur, R. C. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3550-16.2017 · Cited by 302 · see citing works
Self-citationCommon and Dissociable Dysfunction of the Reward System in Bipolar and Unipolar Depression
Satterthwaite, T. D., Kable, J. W., Vandekar, L., Katchmar, N., Bassett, D. S., Baldassano, C. F., Ruparel, K., Elliott, M. A., Sheline, Y. I., Gur, R. C., et al. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.75 · Cited by 249 · see citing works
Self-citationStructural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms
Satterthwaite, T. D., Wolf, D. H., Calkins, M. E., Vandekar, S. N., Erus, G., Ruparel, K., Roalf, D. R., Linn, K. A., Elliott, M. A., Moore, T. M., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.3463 · Cited by 132 · see citing works
Self-citationEvolution of brain network dynamics in neurodevelopment
Chai, L. R., Khambhati, A. N., Ciric, R., Moore, T. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2017) · Network Neuroscience · doi:10.1162/netn_a_00001 · Cited by 100 · see citing works
Lefebvre, S., Demeulemeester, M., Leroy, A., Delmaire, C., Lopes, R., Pins, D., Thomas, P., & Jardri, R. (2016) · Human Brain Mapping · doi:10.1002/hbm.23197 · Cited by 99 · see citing works
Kaczkurkin, A. N., Moore, T. M., Calkins, M. E., Ciric, R., Detre, J. A., Elliott, M. A., Foa, E. B., Garcia de la Garza, A., Roalf, D. R., Rosen, A., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.174 · Cited by 86 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 55
- [1]Singh I Rose N Biomarkers in psychiatry Nature 2009 460 202 207 10.1038/460202a 19587761 · DOI
- [5]Lee SH Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs Nat. Genet. 2013 45 984 994 10.1038/ng.2711 23933821 PMC3800159 · DOI · Paper page
- [8]Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. 10, 186–98 (2009). 19190637
- [9]Bassett DS Sporns O Network neuroscience Nat. Neurosci. 2017 20 353 364 10.1038/nn.4502 28230844 PMC5485642 · DOI · Paper page
- [11]Yeo BTT The organization of the human cerebral cortex estimated by intrinsic functional connectivity J. Neurophysiol. 2011 106 1125 1165 10.1152/jn.00338.2011 21653723 PMC3174820 · DOI · Paper page
- [12]Gordon EM Generation and evaluation of a cortical area parcellation from resting-state correlations Cereb. Cortex 2016 26 288 303 10.1093/cercor/bhu239 25316338 PMC4677978 · DOI · Paper page
- [13]Bassett DS Bullmore ET Human brain networks in health and disease Curr. Opin. Neurol. 2009 22 340 347 10.1097/WCO.0b013e32832d93dd 19494774 PMC2902726 · DOI
- [14]Lynall ME Functional connectivity and brain networks in schizophrenia J. Neurosci. 2010 30 9477 9487 10.1523/JNEUROSCI.0333-10.2010 20631176 PMC2914251 · DOI
- [15]Satterthwaite TD Connectome-wide network analysis of youth with psychosis-spectrum symptoms Mol. Psychiatry 2015 20 1 8 10.1038/mp.2015.66 26033240 PMC4651819 · DOI
- [16]Bassett, D. S., Xia, C. H. & Satterthwaite, T. D. Understanding the emergence of neuropsychiatric disorders with network neuroscience. Biol. Psychiatry Cogn. Neurosci. Neuroimaging In press; (2018) 10.1016/j.bpsc.2018.03.015. PMC6119485 29729890 · DOI
- [17]Kaiser RH Andrews-Hanna JR Wager TD Pizzagalli DA Large-scale network dysfunction in major depressive disorder JAMA Psychiatry 2015 72 603 10.1001/jamapsychiatry.2015.0071 25785575 PMC4456260 · DOI · Paper page
- [18]Li, W. et al. Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment. J. Neurol. Neurosurg. Psychiatry 86, 1097–105 (2015). PMC4465874 25433036
- [19]Alexander-Bloch AF The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia Neuroimage 2012 59 3889 3900 10.1016/j.neuroimage.2011.11.035 22119652 PMC3478383 · DOI
- [20]Rudie JD Altered functional and structural brain network organization in autism Neuroimage Clin. 2013 2 79 94 10.1016/j.nicl.2012.11.006 PMC3777708 24179761 · DOI · Paper page
- [22]Power JD Fair DA Schlaggar BL Petersen SE The development of human functional brain networks Neuron 2010 67 735 748 10.1016/j.neuron.2010.08.017 20826306 PMC2941973 · DOI · Paper page
- [23]Satterthwaite TD Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth Neuroimage 2013 83 45 57 10.1016/j.neuroimage.2013.06.045 23792981 PMC3874413 · DOI · Paper page
- [24]Baum GL Modular segregation of structural brain networks supports the development of executive function in youth Curr. Biol. 2017 27 1561 1572 10.1016/j.cub.2017.04.051 28552358 PMC5491213 · DOI · Paper page
- [25]Paus T Mapping brain maturation and cognitive development during adolescence Trends Cogn. Sci. 2005 9 60 68 10.1016/j.tics.2004.12.008 15668098 · DOI · Paper page
- [26]Insel TR Mental disorders in childhood: shifting the focus from behavioral symptoms to neurodevelopmental trajectories JAMA 2014 311 1727 1728 10.1001/jama.2014.1193 24794359 · DOI
- [27]Calkins ME The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative J. Child Psychol. Psychiatry 2015 12 1356 1369 10.1111/jcpp.12416 PMC4598260 25858255 · DOI · Paper page
- [28]Shanmugan S Common and dissociable mechanisms of executive system dysfunction across psychiatric disorders in youth Am. J. Psychiatry 2016 173 517 526 10.1176/appi.ajp.2015.15060725 26806874 PMC4886342 · DOI · Paper page
- [29]Kaczkurkin AN Elevated amygdala perfusion mediates developmental sex differences in trait anxiety Biol. Psychiatry 2016 80 775 785 10.1016/j.biopsych.2016.04.021 27395327 PMC5074881 · DOI
- [31]Bzdok D Yeo BTT Inference in the age of big data: future perspectives on neuroscience Neuroimage 2017 155 549 564 10.1016/j.neuroimage.2017.04.061 28456584 · DOI
- [33]Witten DM Tibshirani R Hastie T A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis Biostatistics 2009 10 515 534 10.1093/biostatistics/kxp008 19377034 PMC2697346 · DOI
- [34]Avants BB Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population Neuroimage 2014 84 698 711 10.1016/j.neuroimage.2013.09.048 24096125 PMC3911786 · DOI
- [38]Ciric R Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity Neuroimage 2017 154 174 187 10.1016/j.neuroimage.2017.03.020 28302591 PMC5483393 · DOI · Paper page
- [39]Sharp DJ Scott G Leech R Network dysfunction after traumatic brain injury Nat. Rev. Neurol. 2014 10 156 166 10.1038/nrneurol.2014.15 24514870 · DOI
- [42]Insel T Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders Am. J. Psychiatry 2010 167 748 751 10.1176/appi.ajp.2010.09091379 20595427 · DOI · Paper page
- [44]Sharma, A. et al. Common dimensional reward deficits across mood and psychotic disorders: a Connectome-Wide Association Study. Am. J. Psychiatry 174, 657–666 (2017). PMC5495611 28135847
- [46]Fox MD From the cover: the human brain is intrinsically organized into dynamic, anticorrelated functional networks Proc. Natl Acad. Sci. USA 2005 102 9673 9678 10.1073/pnas.0504136102 15976020 PMC1157105 · DOI · Paper page
- [47]Woodward ND Cascio CJ Resting-state functional connectivity in psychiatric disorders JAMA Psychiatry 2015 72 743 744 10.1001/jamapsychiatry.2015.0484 26061674 PMC4693599 · DOI
- [48]Menon V Large-scale brain networks and psychopathology: a unifying triple network model Trends Cogn. Sci. 2011 15 483 506 10.1016/j.tics.2011.08.003 21908230 · DOI · Paper page
- [50]Sheline YI The default mode network and self-referential processes in depression Proc. Natl Acad. Sci. USA 2009 106 1942 1947 10.1073/pnas.0812686106 19171889 PMC2631078 · DOI · Paper page
- [51]Berman MG Depression, rumination and the default network Soc. Cogn. Affect. Neurosci. 2011 6 548 555 10.1093/scan/nsq080 20855296 PMC3190207 · DOI · Paper page
- [52]Whitfield-Gabrieli S Ford JM Default mode network activity and connectivity in psychopathology Annu. Rev. Clin. Psychol. 2012 8 8 49 75 10.1146/annurev-clinpsy-032511-143049 22224834 · DOI · Paper page
- [53]Zhou Y Functional disintegration in paranoid schizophrenia using resting-state fMRI Schizophr. Res. 2007 97 194 205 10.1016/j.schres.2007.05.029 17628434 · DOI
- [54]Pankow A Reduced default mode network connectivity in schizophrenia patients Schizophr. Res. 2015 165 90 93 10.1016/j.schres.2015.03.027 25892719 · DOI
- [55]Lefort-Besnard J Different shades of default mode disturbance in schizophrenia: subnodal covariance estimation in structure and function Hum. Brain. Mapp. 2018 39 644 661 10.1002/hbm.23870 29105239 PMC5764781 · DOI
- [58]Harrow M Carone BJ Westermeyer JF The course of psychosis in eearly phases of schizophrenia Am. J. Psychiatry 1985 142 702 707 10.1176/ajp.142.6.702 4003589 · DOI
- [59]Bongers IL Koot HM Van Der Ende J Verhulst FC Developmental trajectories of externalizing behaviors in childhood and adolescence Child Dev. 2004 75 1523 1537 10.1111/j.1467-8624.2004.00755.x 15369529 · DOI · Paper page
- [61]Kessler RC Epidemiology of women and depression J. Affect Disord. 2003 74 5 13 10.1016/S0165-0327(02)00426-3 12646294 · DOI · Paper page
- [62]Merikangas KR Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A) J. Am. Acad. Child Adolesc. Psychiatry 2010 49 980 989 10.1016/j.jaac.2010.05.017 20855043 PMC2946114 · DOI · Paper page
- [64]Avants BB A reproducible evaluation of ANTs similarity metric performance in brain image registration Neuroimage 2011 54 2033 2044 10.1016/j.neuroimage.2010.09.025 20851191 PMC3065962 · DOI · Paper page
- [65]Wang J Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline Front. Neuroinform. 2014 8 7 10.3389/fninf.2014.00007 24567717 PMC3915103 · DOI
- [66]Tustison NJ Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements Neuroimage 2014 99 166 179 10.1016/j.neuroimage.2014.05.044 24879923 · DOI · Paper page
- [67]Tustison NJ N4ITK: improved N3 bias correction IEEE Trans. Med. Imaging 2010 29 1310 1320 10.1109/TMI.2010.2046908 20378467 PMC3071855 · DOI
- [68]Klein A Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration Neuroimage 2009 46 786 802 10.1016/j.neuroimage.2008.12.037 19195496 PMC2747506 · DOI · Paper page
- [69]Avants BB Tustison NJ Wu J Cook PA Gee JC An open source multivariate framework for n-tissue segmentation with evaluation on public data Neuroinformatics 2011 9 381 400 10.1007/s12021-011-9109-y 21373993 PMC3297199 · DOI
- [70]Greve DN Fischl B Accurate and robust brain image alignment using boundary-based registration Neuroimage 2009 48 63 72 10.1016/j.neuroimage.2009.06.060 19573611 PMC2733527 · DOI
- [71]Hallquist MN Hwang K Luna B The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity Neuroimage 2013 82 208 225 10.1016/j.neuroimage.2013.05.116 23747457 PMC3759585 · DOI · Paper page
- [72]Rosvall M Bergstrom CT Maps of random walks on complex networks reveal community structure Proc. Natl Acad. Sci. USA 2008 105 1118 1123 10.1073/pnas.0706851105 18216267 PMC2234100 · DOI · Paper page
- [75]Mišić, B. et al. Network-level structure-function relationships in human neocortex. Cereb. Cortex26, 3285–96 (2016). PMC4898678 27102654
- [76]Rubinov M Sporns O Complex network measures of brain connectivity: uses and interpretations Neuroimage 2009 52 1059 1069 10.1016/j.neuroimage.2009.10.003 19819337 · DOI · Paper page
- [77]Xia, M., Wang, J. & He, Y. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE8, (2013) e68910. PMC3701683 23861951
- [78]Nassar, R. et al. Gestational age is dimensionally associated with structural brain network abnormalities across development. Cereb. Cortex 10.1093/cercor/bhy091 (2018). PMC6642451 29688290 · DOI
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Dajani, D. R., Burrows, C. A., Odriozola, P., Baez, A., Nebel, M. B., Mostofsky, S. H., & Uddin, L. Q. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101678 · Cited by 33 · see citing works
“In an effort to identify the most fruitful behavioral indices for biomarker identification, future studies may employ large-scale multivariate analyses, such as canonical correlation analysis (Xia et al., 2018), to identify the behavioral metrics that are correlated with neuroimaging metrics.”
Method
- Authors
- Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., Cui, Z., Moore, T. M., Roalf, D. R., Ruparel, K., Wolf, D. H., Davatzikos, C., Gur, R. C., Gur, R. E., Shinohara, R. T., Bassett, D. S., & Satterthwaite, T. D.
- Affiliations
- University of Pennsylvania (US)
- University of Electronic Science and Technology of China (CN)
- Corresponding author
- Satterthwaite, Theodore D.
- ORCID
- Xia, Cedric Huchuan
- Ma, Zongming
- Gu, Shi
- Betzel, Richard F.
- Kaczkurkin, Antonia N.
- Calkins, Monica E.
- Cook, Philip A.
- Vandekar, Simon N.
- Cui, Zaixu
- Moore, Tyler M.
- Roalf, David R.
- Ruparel, Kosha
- Wolf, Daniel H.
- Davatzikos, Christos
- Gur, Ruben C.
- Gur, Raquel E.
- Shinohara, Russell T.
- Bassett, Danielle S.
- Satterthwaite, Theodore D.
- Venue
- Nature Communications
- Published
- 2018-08-01
- Volume
- 9
- Issue
- 1
- Pages
- 3003-3003
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6070480
- OpenAlex
- W2766963537
- Cited by
- 437
- References
- 31
- Citations by year
- 2026: 46 · 2025: 51 · 2024: 63 · 2023: 46 · 2022: 57 · 2021: 69 · 2020: 58 · 2019: 36 · 2018: 11
- FWCI
- 25.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- IUScholarWorks Open (Indiana University) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH107703, R01MH112847, R21MH106799, R01MH107235, MH089983, MH089924, P50MH096891, R01MH101111, K01MH102609, K08MH079364)
- U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (R01EB022573)
- U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (R01NS085211)
- Dowshen Program for Neuroscience
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- functional brain networks, functional connectivity, psychiatric symptoms, mood, psychosis, fear, externalizing problems, default mode network, frontoparietal network, network segregation, developmental changes, sex differences, adolescents, network-based biomarkers, sparse canonical correlation analysis
- MeSH terms
- Adolescent, Adult, Brain, Child, Female, Humans, Male, Nerve Net, Psychopathology, Sex Characteristics, Reproducibility of Results, Cohort Studies, Multivariate Analysis, Young Adult
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