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Parsing neurodevelopmental features of irritability and anxiety: Replication and validation of a latent variable approach

Published online by Cambridge University Press:  08 May 2019

Elise M. Cardinale*
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Katharina Kircanski
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Julia Brooks
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Andrea L. Gold
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Kenneth E. Towbin
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Daniel S. Pine
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Ellen Leibenluft
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
Melissa A. Brotman
Affiliation:
Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
*
Author for Correspondence: Elise M. Cardinale, National Institute of Mental Health, 9000 Rockville Pike, Building 15K, MSC 2670, Bethesda, MD 20892-2670; E-mail: [email protected].

Abstract

Irritability and anxiety are two common clinical phenotypes that involve high-arousal negative affect states (anger and fear), and that frequently co-occur. Elucidating how these two forms of emotion dysregulation relate to perturbed neurodevelopment may benefit from alternate phenotyping strategies. One such strategy applies a bifactor latent variable approach that can parse shared versus unique mechanisms of these two phenotypes. Here, we aim to replicate and extend this approach and examine associations with neural structure in a large transdiagnostic sample of youth (N = 331; M = 13.57, SD = 2.69 years old; 45.92% male). FreeSurfer was used to extract cortical thickness, cortical surface area, and subcortical volume. The current findings replicated the bifactor model and demonstrate measurement invariance as a function of youth age and sex. There were no associations of youth's factor scores with cortical thickness, surface area, or subcortical volume. However, we found strong convergent and divergent validity between parent-reported irritability and anxiety factors with clinician-rated symptoms and impairment. A general negative affectivity factor was robustly associated with overall functional impairment across symptom domains. Together, these results support the utility of the bifactor model as an alternative phenotyping strategy for irritability and anxiety, which may aid in the development of targeted treatments.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2019 

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References

Adleman, N. E., Fromm, S. J., Razdan, V., Kayser, R., Dickstein, D. P., Brotman, M. A., … Leibenluft, E. (2012). Cross-sectional and longitudinal abnormalities in brain structure in children with severe mood dysregulation or bipolar disorder: Cross-sectional and longitudinal volumetric abnormalities in SMD and BD. Journal of Child Psychology and Psychiatry, 53, 11491156. doi:10.1111/j.1469-7610.2012.02568.xGoogle Scholar
American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: Author.Google Scholar
Birmaher, B., Khetarpal, S., Brent, D., Cully, M., Balach, L., Kaufman, J., & Neer, S. M. (1997). The Screen for Child Anxiety Related Emotional Disorders (SCARED): Scale construction and psychometric characteristics. Journal of the American Academy of Child & Adolescent Psychiatry, 36, 545553. doi:10.1097/00004583-199704000-00018Google Scholar
Bonifay, W., Lane, S. P., & Reise, S. P. (2017). Three concerns with applying a bifactor model as a structure of psychopathology. Clinical Psychological Science, 5, 184186. doi:10.1177/2167702616657069Google Scholar
Brotman, M. A., Kircanski, K., Stringaris, A., Pine, D. S., & Leibenluft, E. (2017). Irritability in youths: A translational model. American Journal of Psychiatry, 174, 520532. doi:10.1176/appi.ajp.2016.16070839Google Scholar
Brotman, M. A., Schmajuk, M., Rich, B. A., Dickstein, D. P., Guyer, A. E., Costello, E. J., … Leibenluft, E. (2006). Prevalence, clinical correlates, and longitudinal course of severe mood dysregulation in children. Biological Psychiatry, 60, 991997. doi:10.1016/j.biopsych.2006.08.042Google Scholar
Cardinale, E. M., O'Connell, K., Robertson, E. L., Meena, L. B., Breeden, A. L., Lozier, L. M., … Marsh, A. A. (2018). Callous and uncaring traits are associated with reductions in amygdala volume among youths with varying levels of conduct problems. Psychological Medicine. Advance online publication. doi:10.1017/S0033291718001927Google Scholar
Castellanos-Ryan, N., Struve, M., Whelan, R., Banaschewski, T., Barker, G. J., Bokde, A. L. W., … IMAGEN Consortium. (2014). Neural and cognitive correlates of the common and specific variance across externalizing problems in young adolescence. American Journal of Psychiatry, 171, 13101319. doi:10.1176/appi.ajp.2014.13111499Google Scholar
Copeland, W. E., Brotman, M. A., & Costello, E. J. (2015). Normative irritability in youth: Developmental findings from the Great Smoky Mountains Study. Journal of the American Academy of Child & Adolescent Psychiatry, 54, 635642. doi:10.1016/j.jaac.2015.05.008Google Scholar
Cornacchio, D., Crum, K. I., Coxe, S., Pincus, D. B., & Comer, J. S. (2016). Irritability and severity of anxious symptomatology among youth with anxiety disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 55, 5461. doi:10.1016/j.jaac.2015.10.007Google Scholar
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9, 179194. doi:10.1006/nimg.1998.0395Google Scholar
De Bellis, M. D., Casey, B. J., Dahl, R. E., Birmaher, B., Williamson, D. E., Thomas, K. M., … Ryan, N. D. (2000). A pilot study of amygdala volumes in pediatric generalized anxiety disorder. Biological Psychiatry, 48, 5157. doi:10.1016/S0006-3223(00)00835-0Google Scholar
De Bellis, M. D., Keshavan, M. S., Shifflett, H., Iyengar, S., Dahl, R. E., Axelson, D. A., … Ryan, N. D. (2002). Superior temporal gyrus volumes in pediatric generalized anxiety disorder. Biological Psychiatry, 51, 553562.Google Scholar
De Los Reyes, A., & Kazdin, A. E. (2005). Informant discrepancies in the sssessment of childhood psychopathology: A critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131, 483509. doi:10.1037/0033-2909.131.4.483Google Scholar
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 968980. doi:10.1016/j.neuroimage.2006.01.021Google Scholar
Dickstein, D. P., Milham, M. P., Nugent, A. C., Drevets, W. C., Charney, D. S., Pine, D. S., & Leibenluft, E. (2005). Frontotemporal alterations in pediatric bipolar disorder: Results of a voxel-based morphometry study. Archives of General Psychiatry, 62, 734. doi:10.1001/archpsyc.62.7.734Google Scholar
Fischl, B. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 1122. doi:10.1093/cercor/bhg087Google Scholar
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the USA, 97, 1105011055. doi:10.1073/pnas.200033797Google Scholar
Fischl, B., Salat, D. H., Busa, E., Albert, M. S., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341355. doi:10.1016/S0896-6273(02)00569-XGoogle Scholar
Fischl, B., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004). Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23(Suppl. 1), S69S84. doi:10.1016/j.neuroimage.2004.07.016Google Scholar
Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., … Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 1122.Google Scholar
Gold, A. L., Brotman, M. A., Adleman, N. E., Lever, S. N., Steuber, E. R., Fromm, S. J., … Leibenluft, E. (2016). Comparing brain morphometry across multiple childhood psychiatric disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 55, 10271037. doi:10.1016/j.jaac.2016.08.008Google Scholar
Gold, A. L., Steuber, E. R., White, L. K., Pacheco, J., Sachs, J. F., Pagliaccio, D., … Pine, D. S. (2017). Cortical thickness and subcortical gray matter volume in pediatric anxiety disorders. Neuropsychopharmacology. Advance online publication. doi:10.1038/npp.2017.83Google Scholar
Grimm, O., Pohlack, S., Cacciaglia, R., Winkelmann, T., Plichta, M. M., Demirakca, T., & Flor, H. (2015). Amygdalar and hippocampal volume: A comparison between manual segmentation, Freesurfer and VBM. Journal of Neuroscience Methods, 253, 254261. doi:10.1016/j.jneumeth.2015.05.024Google Scholar
Hafeman, D. M., Chang, K. D., Garrett, A. S., Sanders, E. M., & Phillips, M. L. (2012). Effects of medication on neuroimaging findings in bipolar disorder: An updated review: Medication effects on neuroimaging in bipolar disorder. Bipolar Disorders, 14, 375410. doi:10.1111/j.1399-5618.2012.01023.xGoogle Scholar
Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., … Fischl, B. (2006). Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImage, 32, 180194. doi:10.1016/j.neuroimage.2006.02.051Google Scholar
Hayes, A. F. (2016). The PROCESS macro for SPSS and SAS. Retrieved from http://www.processmacro.org/indexGoogle Scholar
Holmes, A. P., Blair, R. C., Watson, J. D. G., & Ford, I. (1996). Nonparametric analysis of statistic images from functional mapping experiments. Journal of Cerebral Blood Flow and Metabolism, 16, 722.Google Scholar
Hyman, S. E. (2007). Can neuroscience be integrated into the DSM-V? Nature Reviews Neuroscience, 8, 725732. doi:10.1038/nrn2218Google Scholar
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., … Wang, P. (2010). Research Domain Criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167, 748751. doi:10.1176/appi.ajp.2010.09091379Google Scholar
Kircanski, K., White, L. K., Tseng, W.-L., Wiggins, J. L., Frank, H. R., Sequeira, S., … Brotman, M. A. (2018). A latent variable approach to differentiating neural mechanisms of irritability and anxiety in youth. JAMA Psychiatry, 75, 631. doi:10.1001/jamapsychiatry.2018.0468Google Scholar
Kuperberg, G. R., McGuire, P. K., Ozawa, F., Goff, D., West, W. C., & Williams, S. C. R. (2003). Regionally localized thinning of the cerebral cortex in schizophrenia. Archives of General Psychoiatry, 60, 11.Google Scholar
Leadbeater, B. J., & Homel, J. (2015). Irritable and defiant sub-dimensions of ODD: Their stability and prediction of internalizing symptoms and conduct problems from adolescence to young adulthood. Journal of Abnormal Child Psychology, 43, 407421. doi:10.1007/s10802-014-9908-3Google Scholar
Leibenluft, E. (2017). Pediatric irritability: A systems neuroscience approach. Trends in Cognitive Sciences, 21, 277289. doi:10.1016/j.tics.2017.02.002Google Scholar
Liao, M., Yang, F., Zhang, Y., He, Z., Song, M., Jiang, T., … Li, L. (2013). Childhood maltreatment is associated with larger left thalamic gray matter volume in adolescents with generalized anxiety disorder. PLOS ONE, 8, e71898. doi:10.1371/journal.pone.0071898Google Scholar
McLaughlin, K. A., Sheridan, M. A., Winter, W., Fox, N. A., Zeanah, C. H., & Nelson, C. A. (2014). Widespread reductions in cortical thickness following severe early-life deprivation: A neurodevelopmental pathway to attention-deficit/hyperactivity disorder. Biological Psychiatry, 76, 629638. doi:10.1016/j.biopsych.2013.08.016Google Scholar
Merz, E. C., He, X., & Noble, K. G. (2018). Anxiety, depression, impulsivity, and brain structure in children and adolescents. NeuroImage: Clinical, 20, 243251. doi:10.1016/j.nicl.2018.07.020Google Scholar
Milham, M. P., Nugent, A. C., Drevets, W. C., Dickstein, D. S., Leibenluft, E., Ernst, M., … Pine, D. S. (2005). Selective reduction in amygdala volume in pediatric anxiety disorders: A voxel-based morphometry investigation. Biological Psychiatry, 57, 961966. doi:10.1016/j.biopsych.2005.01.038Google Scholar
Morey, R. A., Petty, C. M., Xu, Y., Hayes, J. P., Wagner, H. R., Lewis, D. V., … McCarthy, G. (2009). A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. NeuroImage, 45, 855866. doi:10.1016/j.neuroimage.2008.12.033Google Scholar
Morey, R. A., Selgrade, E. S., Wagner, H. R., Huettel, S. A., Wang, L., & McCarthy, G. (2010). Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. Human Brain Mapping, 31, 17511763. doi:10.1002/hbm.20973Google Scholar
Morgan, G., Hodge, K., Wells, K., & Watkins, M. (2015). Are fit indices biased in favor of bi-factor models in cognitive ability research? A comparison of fit in correlated factors, higher-order, and bi-factor models via Monte Carlo simulations. Journal of Intelligence, 3, 220. doi:10.3390/jintelligence3010002Google Scholar
Morris, S. E., & Cuthbert, B. N. (2012). Research Domain Criteria: Cognitive systems, neural circuits, and dimensions of behavior. Dialogues in Clinical Neuroscience, 14, 2937.Google Scholar
Mueller, S. C., Aouidad, A., Gorodetsky, E., Goldman, D., Pine, D. S., & Ernst, M. (2013). Grey matter volume in adolescent anxiety: An impact of the brain-derived neurotropic factor Val66Met polymorphism? Journal of the American Academy of Child & Adolescent Psychiatry, 52, 184195. doi:10.1016/j.jaac.2012.11.016Google Scholar
Murray, A. L., & Johnson, W. (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence, 41, 407422. doi:10.1016/j.intell.2013.06.004Google Scholar
Navari, S., & Dazzan, P. (2009). Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychological Medicine, 39, 1763. doi:10.1017/S0033291709005315Google Scholar
Newman, E., Thompson, W. K., Bartsch, H., Hagler, D. J., Chen, C.-H., Brown, T. T., … Jernigan, T. L. (2015). Anxiety is related to indices of cortical maturation in typically developing children and adolescents. Brain Structure and Function, 221, 30133025. doi:10.1007/s00429-015-1085-9Google Scholar
Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15, 125. doi:10.1002/hbm.1058Google Scholar
Ostby, Y., Tamnes, C. K., Fjell, A. M., Westlye, L. T., Due-Tønnessen, P., & Walhovd, K. B. (2009). Heterogeneity in subcortical brain development: A structural magnetic resonance imaging study of brain maturation from 8 to 30 years. Journal of Neuroscience, 29, 1177211782. doi:10.1523/JNEUROSCI.1242-09.2009Google Scholar
Pine, D. S. (2007). Research Review: A neuroscience framework for pediatric anxiety disorders. Journal of Child Psychology and Psychiatry, 48, 631648. doi:10.1111/j.1469-7610.2007.01751.xGoogle Scholar
Qin, S., Young, C. B., Duan, X., Chen, T., Supekar, K., & Menon, V. (2014). Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood. Biological Psychiatry, 75, 892900. doi:10.1016/j.biopsych.2013.10.006Google Scholar
Research Units On Pediatric Psychopharmacology Anxiety Study Group. (2002). The Pediatric Anxiety Rating Scale (PARS): Development and psychometric properties. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 10611069. doi:10.1097/00004583-200209000-00006Google Scholar
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage, 61, 14021418. doi:10.1016/j.neuroimage.2012.02.084Google Scholar
Rosas, H. D., Liu, A. K., Hersch, S., Glessner, M., Ferrante, R. J., Salat, D. H., … Fischl, B. (2002). CME regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology, 58, 695701.Google Scholar
Savage, J., Verhulst, B., Copeland, W., Althoff, R. R., Lichtenstein, P., & Roberson-Nay, R. (2015). A genetically informed study of the longitudinal relation between irritability and anxious/depressed symptoms. Journal of the American Academy of Child & Adolescent Psychiatry, 54, 377384. doi:10.1016/j.jaac.2015.02.010Google Scholar
Schoemaker, D., Buss, C., Head, K., Sandman, C. A., Davis, E. P., Chakravarty, M. M., … Pruessner, J. C. (2016). Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation. NeuroImage, 129, 114. doi:10.1016/j.neuroimage.2016.01.038Google Scholar
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D., Hahn, H. K., & Fischl, B. (2004). A hybrid approach to the skull stripping problem in MRI. NeuroImage, 22, 10601075. doi:10.1016/j.neuroimage.2004.03.032Google Scholar
Shaffer, D. (1983). A Children's Global Assessment Scale (CGAS). Archives of General Psychiatry, 40, 1228. doi:10.1001/archpsyc.1983.01790100074010Google Scholar
Shanmugan, S., Wolf, D. H., Calkins, M. E., Moore, T. M., Ruparel, K., Hopson, R. D., … Satterthwaite, T. D. (2016). Common and dissociable mechanisms of executive system dysfunction across psychiatric disorders in youth. American Journal of Psychiatry, 173, 517526. doi:10.1176/appi.ajp.2015.15060725Google Scholar
Shaw, P., Stringaris, A., Nigg, J., & Leibenluft, E. (2014). Emotion dysregulation in attention deficit hyperactivity disorder. American Journal of Psychiatry, 171, 276293. doi:10.1176/appi.ajp.2013.13070966Google Scholar
Sheridan, M. A., Fox, N. A., Zeanah, C. H., McLaughlin, K. A., & Nelson, C. A. (2012). Variation in neural development as a result of exposure to institutionalization early in childhood. Proceedings of the National Academy of Sciences of the USA, 109, 1292712932. doi:10.1073/pnas.1200041109Google Scholar
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17, 8797. doi:10.1109/42.668698Google Scholar
Smieskova, R., Fusar-Poli, P., Allen, P., Bendfeldt, K., Stieglitz, R., Drewe, J., … Borgwardt, S. (2009). The effects of antipsychotics on the brain: What have we learnt from structural imaging of schizophrenia?—A systematic review. Current Pharmaceutical Design, 15, 25352549. doi:10.2174/138161209788957456Google Scholar
Smith, S., & Nichols, T. (2009). Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44, 8398. doi:10.1016/j.neuroimage.2008.03.061Google Scholar
Stoddard, J., Stringaris, A., Brotman, M. A., Montville, D., Pine, D. S., & Leibenluft, E. (2014). Irritability in child and adolescent anxiety disorders. Depression and Anxiety, 31, 566573. doi:10.1002/da.22151Google Scholar
Strawn, J. R., Chu, W.-J., Whitsel, R. M., Weber, W. A., Norris, M. M., Adler, C. M., … DelBello, M. P. (2013). A pilot study of anterior cingulate cortex neurochemistry in adolescents with generalized anxiety disorder. Neuropsychobiology, 67, 224229. doi:10.1159/000347090Google Scholar
Strawn, J. R., Hamm, L., Fitzgerald, D. A., Fitzgerald, K. D., Monk, C. S., & Phan, K. L. (2015). Neurostructural abnormalities in pediatric anxiety disorders. Journal of Anxiety Disorders, 32, 8188. doi:10.1016/j.janxdis.2015.03.004Google Scholar
Strawn, J. R., John Wegman, C., Dominick, K. C., Swartz, M. S., Wehry, A. M., Patino, L. R., … DelBello, M. P. (2014). Cortical surface anatomy in pediatric patients with generalized anxiety disorder. Journal of Anxiety Disorders, 28, 717723. doi:10.1016/j.janxdis.2014.07.012Google Scholar
Stringaris, A., & Goodman, R. (2009). Three dimensions of oppositionality in youth. Journal of Child Psychology and Psychiatry, 50, 216223. doi:10.1111/j.1469-7610.2008.01989.xGoogle Scholar
Stringaris, A., Goodman, R., Ferdinando, S., Razdan, V., Muhrer, E., Leibenluft, E., & Brotman, M. A. (2012). The Affective Reactivity Index: A concise irritability scale for clinical and research settings. Journal of Child Psychology and Psychiatry, 53, 11091117. doi:10.1111/j.1469-7610.2012.02561.xGoogle Scholar
Stringaris, A., Maughan, B., Copeland, W. S., Costello, E. J., & Angold, A. (2013). Irritable mood as a symptom of depression in youth: Prevalence, developmental, and clinical correlates in the Great Smoky Mountains Study. Journal of the American Academy of Child & Adolescent Psychiatry, 52, 831840. doi:10.1016/j.jaac.2013.05.017Google Scholar
Sylvester, C. M., Barch, D. M., Harms, M. P., Belden, A. C., Oakberg, T. J., Gold, A. L., … Pine, D. S. (2016). Early childhood behavioral inhibition predicts cortical thickness in adulthood. Journal of the American Academy of Child & Adolescent Psychiatry, 55, 122129. doi:10.1016/j.jaac.2015.11.007Google Scholar
Vidal-Ribas, P., Brotman, M. A., Valdivieso, I., Leibenluft, E., & Stringaris, A. (2016). The status of irritability in psychiatry: A conceptual and quantitative review. Journal of the American Academy of Child & Adolescent Psychiatry, 55, 556570. doi:10.1016/j.jaac.2016.04.014Google Scholar
Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96, 465490.Google Scholar
Wiggins, J. L., Brotman, M. A., Adleman, N. E., Kim, P., Oakes, A. H., Reynolds, R. C., … Leibenluft, E. (2016). Neural correlates of irritability in disruptive mood dysregulation and bipolar disorders. American Journal of Psychiatry, 173, 722730. doi:10.1176/appi.ajp.2015.15060833Google Scholar
Winkler, A. M., Greve, D. N., Bjuland, K. J., Nichols, T. E., Sabuncu, M. R., Håberg, A. K., … Rimol, L. M. (2018). Joint Analysis of Cortical Area and Thickness as a Replacement for the Analysis of the Volume of the Cerebral Cortex. Cerebral Cortex, 28(2), 738749. https://doi.org/10.1093/cercor/bhx308Google Scholar
Winkler, A. M., Ridgway, G. R., Douaud, G., Nichols, T. E., & Smith, S. M. (2016). Faster permutation inference in brain imaging. NeuroImage, 141, 502516.Google Scholar
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381397. doi:10.1016/j.neuroimage.2014.01.060Google Scholar
Winkler, A. M., Webster, M. A., Brooks, J. C., Tracey, I., Smith, S. M., & Nichols, T. E. (2016). Non-parametric combination and related permutation tests for neuroimaging: NPC and related permutation tests for neuroimaging. Human Brain Mapping, 37, 14861511. doi:10.1002/hbm.23115Google Scholar
Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E., & Smith, S. M. (2015). Multi-level block permutation. NeuroImage, 123, 253268. doi:10.1016/j.neuroimage.2015.05.092Google Scholar
Zald, D. H., & Lahey, B. B. (2017). Implications of the hierarchical structure of psychopathology for psychiatric neuroimaging. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 2, 310317. doi:10.1016/j.bpsc.2017.02.003Google Scholar
Zisner, A., & Beauchaine, T. P. (2016). Neural substrates of trait impulsivity, anhedonia, and irritability: Mechanisms of heterotypic comorbidity between externalizing disorders and unipolar depression. Development and Psychopathology, 28(4, Pt. 1), 11771208. doi:10.1017/S0954579416000754Google Scholar