Abstract
Life satisfaction is a component of subjective well-being that reflects a global judgement of the quality of life according to an individual’s own needs and expectations. As a psychological construct, it has attracted attention due to its relationship to mental health, resilience to stress, and other factors. Neuroimaging studies have identified neurobiological correlates of life satisfaction; however, they are limited to functional connectivity and gray matter morphometry. We explored features of gray matter microstructure obtained through compartmental modeling of multi-shell diffusion MRI data, and we examined cortical microstructure in frontoinsular cortex in a cohort of 807 typical young adults scanned as part of the Human Connectome Project. Our experiments identified the orientation dispersion index (ODI), and analogously fractional anisotropy (FA), of frontoinsular cortex as a robust set of anatomically-specific lateralized diffusion MRI microstructure features that are linked to life satisfaction, independent of other demographic, socioeconomic, and behavioral factors. We further validated our findings in a secondary test-retest dataset and found high reliability of our imaging metrics and reproducibility of outcomes. In our analysis of twin and non-twin siblings, we found basic microstructure in frontoinsular cortex to be strongly genetically determined. We also found a more moderate but still very significant genetic role in determining microstructure as it relates to life satisfaction in frontoinsular cortex. Our findings suggest a potential linkage between well-being and microscopic features of frontoinsular cortex, which may reflect cellular morphology and architecture and may more broadly implicate the integrity of the homeostatic processing performed by frontoinsular cortex as an important component of an individual’s judgements of life satisfaction.
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Availability of data and material
Data used in our study is available with permission from the Human Connectome Project Footnote 2. Our data image analysis and visualization tools available online as part of the Quantitative Imaging Toolkit (QIT) Footnote 3Footnote 4.
References
Abdellaoui, A., de Moor, M.H., Geels, L.M., Van Beek, J.H., Willemsen, G., & D. I. Boomsma. (2012). Thought problems from adolescence to adulthood: measurement invariance and longitudinal heritability. Behavior Genetics, 42(1), 19–29.
Achenbach, T.M., & Rescorla, L. (2003). Manual for the ASEBA adult forms & profiles.
Alexander, D.C., Dyrby, T.B., Nilsson, M., & Zhang, H. (2019). Imaging brain microstructure with diffusion mri: practicality and applications. NMR in Biomedicine, 32(4), e3841.
Allman, J.M., Tetreault, N.A., Hakeem, A.Y., Manaye, K.F., Semendeferi, K., Erwin, J.M., Park, S., Goubert, V., & Hof, P.R. (2010). The von Economo neurons in frontoinsular and anterior cingulate cortex in great apes and humans. Brain Structure and Function, 214(5-6), 495–517.
Avants, B.B., Epstein, C.L., Grossman, M., & Gee, J.C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41.
Bartels, M. (2015). Genetics of wellbeing and its components satisfaction with life, happiness, and quality of life: A review and meta-analysis of heritability studies. Behavior Genetics, 45(2), 137–156.
Basser, P.J., & Jones, D.K. (2002). Diffusion-tensor MRI: theory, experimental design and data analysis–a technical review. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo, 15(7-8), 456–467.
Baxi, M., Di Biase, M.A., Lyall, A.E., Cetin-Karayumak, S., Seitz, J., Ning, L., Makris, N., Rosene, D., Kubicki, M., & Rathi, Y. (2020). Quantifying genetic and environmental influence on gray matter microstructure using diffusion mri. Cerebral Cortex.
Broad, R.J., Gabel, M.C., Dowell, N.G., Schwartzman, D.J., Seth, A.K., Zhang, H., Alexander, D.C., Cercignani, M., & Leigh, P.N. (2019). Neurite orientation and dispersion density imaging (noddi) detects cortical and corticospinal tract degeneration in als. Journal of Neurology Neurosurgery & Psychiatry, 90 (4), 404–411.
Cabeen, R.P, Laidlaw, D.H, & Toga, A.W. (2018). Quantitative Imaging Toolkit: Software for Interactive 3D Visualization, Data Exploration, and Computational Analysis of Neuroimaging Datasets. In Proc International Society for Magnetic Resonance in Medicine (ISMRM), Vol. 2018:2854.
Cabeen, R., Sepehrband, F., & Toga, A. (2019). Rapid and Accurate NODDI Parameter Estimation with the Spherical Mean Technique. In Proc International Society for Magnetic Resonance in Medicine (ISMRM), (Vol. 2019 p. 3363).
Cabeen, R.P., Allman, J.M., & Toga, A.W. (2020). THC exposure is reflected in the microstructure of the cerebral cortex and amygdala of young adults. Cerebral cortex.
Caron, B., Bullock, D., Kitchell, L., McPherson, B.C., Kellar, D.A., Cheng, H., Newman, S.D., Port, N.L., & Pestilli, F. (2020). Human white matter microstructure predicts elite sports participation.
Clark, D.L., Boutros, N.N., & Mendez, M.F. (2010). The Brain and Behavior: An Introduction to Behavioral Neuroanatomy, Cambridge University Press, Cambridge.
Craig, A. (2009). How do you feel–now? the anterior insula and human awareness. Nature Reviews Neuroscience, 10(1), 59–70.
Craig, A. (2011). Significance of the insula for the evolution of human awareness of feelings from the body. Annals of the New York Academy of Sciences, 1225(1), 72–82.
Critchley, H.D. (2005). Neural mechanisms of autonomic, affective, and cognitive integration. Journal of Comparative Neurology, 493(1), 154–166.
Diener, E. (2009). Subjective well-being. In The science of well-being (pp. 11–58): Springer.
Diener, E., & Seligman, M.E. (2004). Beyond money: toward an economy of well-being. Psychological Science in the Public Interest, 5(1), 1–31.
Diener, E., Emmons, R.A., Larsen, R.J., & Griffin, S. (1985). The satisfaction with life scale, (Vol. 49 . https://doi.org/10.1207/s15327752jpa4901_13. PMID: 16367493.
Dinov, I., Van Horn, J., Lozev, K., Magsipoc, R., Petrosyan, P., Liu, Z., MacKenzie-Graha, A., Eggert, P., Parker, D.S., & Toga, A.W. (2009). Efficient, distributed and interactive neuroimaging data analysis using the loni pipeline. Frontiers in Neuroinformatics, 3, 22.
Fischl, B. (2012). Freesurfer. NeuroImage, 62(2), 774–781.
Fujita, F., & Diener, E. (2005). Life satisfaction set point: stability and change. Journal of Personality and Social Psychology, 88(1), 158.
Fukutomi, H., Glasser, M.F., Zhang, H., Autio, J.A., Coalson, T.S., Okada, T., Togashi, K., Van Essen, D.C., & Hayashi, T. (2018). Neurite imaging reveals microstructural variations in human cerebral cortical gray matter. Neuroimage, 182, 488– 499.
Gefen, T., Papastefan, S.T., Rezvanian, A., Bigio, E.H., Weintraub, S., Rogalski, E., Mesulam, M.-M., & Geula, C. (2018). Von economo neurons of the anterior cingulate across the lifespan and in alzheimer’s disease. Cortex, 99, 69–77.
Genç, E., Fraenz, C., Schlüter, C., Friedrich, P., Hossiep, R., Voelkle, M.C., Ling, J.M., Güntürkün, O., & Jung, R.E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communications, 9(1), 1–11.
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., & et al. (2013). The minimal preprocessing pipelines for the human connectome project. Neuroimage, 80, 105–124.
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). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171.
Gong, T., Tong, Q., He, H., Sun, Y., Zhong, J., & Zhang, H. (2020). Mte-noddi: Multi-te noddi for disentangling non-t2-weighted signal fractions from compartment-specific t2 relaxation times. NeuroImage, 217, 116906.
Goubran, M., Leuze, C., Hsueh, B., Aswendt, M., Ye, L., Tian, Q., Cheng, M.Y., Crow, A., Steinberg, G.K., McNab, J.A., & et al. (2019). Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to mri. Nature Communications, 10(1), 1–17.
Gu, X., Hof, P.R., Friston, K.J., & Fan, J. (2013). Anterior insular cortex and emotional awareness. Journal of Comparative Neurology, 521(15), 3371–3388.
Guerrero, J.M., Adluru, N., Bendlin, B.B., Goldsmith, H.H., Schaefer, S.M., Davidson, R.J., Kecskemeti, S.R., Zhang, H., & Alexander, A.L. (2019). Optimizing the intrinsic parallel diffusivity in noddi: An extensive empirical evaluation. PloS one, 14(9), e0217118.
Hagslätt, H., Nilsson, M., Hansson, H., Lätt, J., & van Westen, D. (2010).
Hlavac, M. (2013). Stargazer: Latex code and ascii text for well-formatted regression and summary statistics tables. http://CRAN.R-project.org/package=stargazer.
Hoy, A.R., Koay, C.G., Kecskemeti, S.R., & Alexander, A.L. (2014). Optimization of a free water elimination two-compartment model for diffusion tensor imaging. Neuroimage, 103, 323–333.
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., & Smith, S.M. (2012). FSL. Neuroimage, 62(2), 782–790.
Kim, E.-J., Sidhu, M., Gaus, S.E., Huang, E.J., Hof, P.R., Miller, B.L., DeArmond, S.J., & Seeley, W.W. (2012). Selective frontoinsular von economo neuron and fork cell loss in early behavioral variant frontotemporal dementia. Cerebral Cortex, 22(2), 251–259.
Kim, E.J., Kyeong, S., Cho, S.W., Chun, J.-W., Park, H.-J., Kim, J., Kim, J., Dolan, R.J., & Kim, J.-J. (2016). Happier people show greater neural connectivity during negative self-referential processing. PloS one, 11(2), e0149554.
Kong, F., & You, X. (2013). Loneliness and self-esteem as mediators between social support and life satisfaction in late adolescence. Social Indicators Research, 110(1), 271–279.
Kong, F., Ding, K., Yang, Z., Dang, X., Hu, S., Song, Y., & Liu, J. (2015a). Examining gray matter structures associated with individual differences in global life satisfaction in a large sample of young adults. Social Cognitive and Affective Neuroscience, 10(7), 952–960.
Kong, F., Hu, S., Wang, X., Song, Y., & Liu, J. (2015b). Neural correlates of the happy life: the amplitude of spontaneous low frequency fluctuations predicts subjective well-being. Neuroimage, 107, 136–145.
Kong, F., Liu, L., Wang, X., Hu, S., Song, Y., & Liu, J. (2015c). Different neural pathways linking personality traits and eudaimonic well-being: a resting-state functional magnetic resonance imaging study. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 299–309.
Kong, F., Wang, X., Hu, S., & Liu, J. (2015d). Neural correlates of psychological resilience and their relation to life satisfaction in a sample of healthy young adults. NeuroImage, 123, 165–172.
Kubiszewski, I., Zakariyya, N., Costanza, R., & Jarvis, D. (2020). Resilience of self-reported life satisfaction: A case study of who conforms to set-point theory in australia. PloS one, 15(8), e0237161.
Kyeong, S., Kim, J., Kim, J., Kim, E.J., Kim, H.E., & Kim, J.-J. (2020). Differences in the modulation of functional connectivity by self-talk tasks between people with low and high life satisfaction. NeuroImage, 11(2), 116929.
Lewis, G.J., Kanai, R., Rees, G., & Bates, T.C. (2014). Neural correlates of the ’good life’: eudaimonic well-being is associated with insular cortex volume. Social Cognitive and Affective Neuroscience, 9(5), 615–618.
Li, R., Zhu, X., Zheng, Z., Wang, P., & Li, J. (2020). Subjective well-being is associated with the functional connectivity network of the dorsal anterior insula. Neuropsychologia, 141, 107393.
Lydon-Staley, D., Kuehner, C., Zamoscik, V., Huffziger, S., Kirsch, P., & Bassett, D. (2019). Repetitive negative thinking in daily life and functional connectivity among default mode, fronto-parietal, and salience networks. Translational Psychiatry, 9(1), 1–12.
Lykken, D., & Tellegen, A. (1996). Happiness is a stochastic phenomenon. Psychological Science, 7(3), 186–189.
Machado, L., & Cantilino, A. (2017). Neural correlates of wellbeing scales Preliminary data. The Australian and New Zealand journal of Psychiatry, 51(9), 946.
Mahmoud, J.S.R., Staten, R., Hall, L.A., & Lennie, T.A. (2012). The relationship among young adult college students’ depression, anxiety, stress, demographics, life satisfaction, and coping styles. Issues in Mental Health Nursing, 33(3), 149–156.
Melin, R., Fugl-Meyer, K.S., & Fugl-Meyer, A.R. (2003). Life satisfaction in 18-to 64-year-old swedes: in relation to education, employment situation, health and physical activity. Journal of rehabilitation medicine, 35(2), 84–90.
Nazeri, A., Schifani, C., Anderson, J.A., Ameis, S.H., & Voineskos, A.N. (2020). In vivo imaging of gray matter microstructure in major psychiatric disorders: Opportunities for clinical translation. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging,.
Nieuwenhuys, R. (2012). The insular cortex: a review. In Progress in Brain Research, (Vol. 195 pp. 123–163): Elsevier.
Nimchinsky, E.A., Gilissen, E., Allman, J.M., Perl, D.P., Erwin, J.M., & Hof, P.R. (1999). A neuronal morphologic type unique to humans and great apes. Proceedings of the National Academy of Sciences, 96(9), 5268–5273.
Ourry, V., Gonneaud, J., Landeau, B., Moulinet, I., Touron, E., Dautricourt, S., Le Du, G., Mézenge, F., André, C., Bejanin, A., & et al. (2021). Association of quality of life with structural, functional and molecular brain imaging in community-dwelling older adults. NeuroImage, 231, 117819.
Pauli, W.M., Nili, A.N., & Tyszka, J.M. (2018). A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Scientific Data, 180063, 5.
Pavot, W., & Diener, E. (2009). Review of the satisfaction with life scale, Springer.
Peterson, C., Park, N., & Seligman, M.E. (2005). Orientations to happiness and life satisfaction: The full life versus the empty life. Journal of Happiness Studies, 6(1), 25–41.
Ryan, R.M., & Deci, E.L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166.
Ryff, C.D., Singer, B.H., & Dienberg Love, G. (2004). Positive health: connecting well–being with biology. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 359(1449), 1383–1394.
Salsman, J.M., Butt, Z., Pilkonis, P.A., Cyranowski, J.M., Zill, N., Hendrie, H.C., Kupst, M.J., Kelly, M.A., Bode, R.K., Choi, S.W., & et al. (2013). Emotion assessment using the nih toolbox. Neurology, 80(11 Supplement 3), S76–S86.
Salsman, J.M., Lai, J.-S., Hendrie, H.C., Butt, Z., Zill, N., Pilkonis, P.A., Peterson, C., Stoney, C.M., Brouwers, P., & Cella, D. (2014). Assessing psychological well-being: self-report instruments for the nih toolbox. Quality of Life Research, 23(1), 205–215.
Samman, E. (2007). Psychological and subjective well-being: A proposal for internationally comparable indicators. Oxford Development Studies, 35(4), 459–486.
Sato, W., Kochiyama, T., Uono, S., Kubota, Y., Sawada, R., Yoshimura, S., & Toichi, M. (2015). The structural neural substrate of subjective happiness. Scientific Reports, 5(1), 16891.
Schmitz, J., Fraenz, C., Schlüter, C., Friedrich, P., Jung, R.E., Güntürkün, O., Genç, E., & Ocklenburg, S. (2019). Hemispheric asymmetries in cortical gray matter microstructure identified by neurite orientation dispersion and density imaging. Neuroimage, 189, 667–675.
Seeley, W.W., Carlin, D.A., Allman, J.M., Macedo, M.N., Bush, C., Miller, B.L., & DeArmond, S.J. (2006). Early frontotemporal dementia targets neurons unique to apes and humans. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 60(6), 660–667.
Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., & Greicius, M.D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356.
Seeley, W.W., Merkle, F.T., Gaus, S.E., Craig, A., Allman, J.M., Hof, P.R., & Economo, C. (2011). Distinctive neurons of the anterior cingulate and frontoinsular cortex: a historical perspective. Cereb. Cortex, 22(2), 245–250.
Sepehrband, F., Cabeen, R.P., Choupan, J., Barisano, G., Law, M., Toga, A.W., Initiative, A.D.N., & et al. (2019). Perivascular space fluid contributes to diffusion tensor imaging changes in white matter. Neuroimage, 197, 243–254.
Shin, D.C., & Johnson, D.M. (1978). Avowed happiness as an overall assessment of the quality of life. Social indicators research, 5(1-4), 475–492.
Sotiropoulos, S.N., Jbabdi, S., Xu, J., Andersson, J.L., Moeller, S., Auerbach, E.J., Glasser, M.F., Hernandez, M., Sapiro, G., Jenkinson, M., & et al. (2013). Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage, 80, 125–143.
Tanzer, J.R., & Weyandt, L. (2019). Imaging happiness: Meta analysis and review. Journal of Happiness Studies, 21, 2693–2734.
Torso, M., Bozzali, M., Zamboni, G., Jenkinson, M., Chance, S.A., & Alzheimers Disease Neuroimage Initiative. (2021). Detection of Alzheimer’s Disease using cortical diffusion tensor imaging. Human Brain Mapping, 42(4), 967–977.
Tyszka, J.M., & Pauli, W.M. (2016). In vivo delineation of subdivisions of the human amygdaloid complex in a high-resolution group template. Human Brain Mapping, 37(11), 3979–3998.
Ullsperger, M., Harsay, H.A., Wessel, J.R., & Ridderinkhof, K.R. (2010). Conscious perception of errors and its relation to the anterior insula. Brain Structure and Function, 214(5), 629–643.
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., Consortium, W.-M.H.C., & et al. (2013). The WU-Minn human connectome project: an overview. NeuroImage, 80, 62–79.
Van’t Ent, D., den Braber, A., Baselmans, B.M.L., Brouwer, R.M., Dolan, C.V., Hulshoff Pol, H.E., de Geus, E.J.C., & Bartels, M. (2017). Associations between subjective well-being and subcortical brain volumes. Scientific reports, 7(1), 6957.
Watson, K.K., Jones, T.K., & Allman, J.M. (2006). Dendritic architecture of the von Economo neurons. Neuroscience, 141(3), 1107– 1112.
Watson, K.K., Matthews, B.J., & Allman, J.M. (2007). Brain activation during sight gags and language-dependent humor. Cerebral Cortex, 17(2), 314–324.
Westin, C.-F., Knutsson, H., Pasternak, O., Szczepankiewicz, F., Özarslan, E., van Westen, D., Mattisson, C., Bogren, M., O’Donnell, L.J., Kubicki, M., & et al. (2016). Q-space trajectory imaging for multidimensional diffusion mri of the human brain. Neuroimage, 135, 345–362.
Wickham, H. (2017). The tidyverse. R package ver., 1(1), 1.
Yi, S.Y., Barnett, B.R., Torres-Velázquez, M., Zhang, Y., Hurley, S.A., Rowley, P.A., Hernando, D., & Yu, J. -P. J. (2019). Detecting microglial density with quantitative multi-compartment diffusion mri. Frontiers in Neuroscience, 13, 81.
Zhang, H., Yushkevich, P.A., Alexander, D.C., & Gee, J.C. (2006). Deformable registration of diffusion tensor mr images with explicit orientation optimization. Medical Image Analysis, 10(5), 764–785.
Zhang, H., Avants, B.B., Yushkevich, P.A., Woo, J.H., Wang, S., McCluskey, L.F., Elman, L.B., Melhem, E.R., & Gee, J.C. (2007). High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis. IEEE Transactions on Medical Imaging, 26(11), 1585–1597.
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., & Alexander, D.C. (2012). Noddi: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4), 1000–1016.
Zhang, S., Peng, H., Dawe, R.J., & Arfanakis, K. (2011). Enhanced ICBM diffusion tensor template of the human brain. Neuroimage, 54(2), 974–984.
Zhu, X., Wang, K., Chen, L., Cao, A., Chen, Q., Li, J., & Qiu, J. (2018). Together Means More Happiness Relationship Status Moderates the Association between Brain Structure and Life Satisfaction. Neuroscience, 384, 406–416.
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This work was supported by National Institutes of Health (grant number P41EB015922) and made possible in part by grant number 2020-225670 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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Cabeen, R.P., Toga, A.W. & Allman, J.M. Frontoinsular cortical microstructure is linked to life satisfaction in young adulthood. Brain Imaging and Behavior 15, 2775–2789 (2021). https://doi.org/10.1007/s11682-021-00467-y
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DOI: https://doi.org/10.1007/s11682-021-00467-y