232
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data

, , , , &
Pages 414-427 | Received 20 Nov 2021, Published online: 07 Oct 2022

References

  • Abbott, R. A., Croudace, T. J., Ploubidis, G. B., Kuh, D., Richards, M., & Huppert, F.A. (2008). The relationship between early personality and midlife psychological well-being: Evidence from a UK birth cohort study. Social Psychiatry and Psychiatric Epidemiology, 43(9), 679–687.
  • Achenbach, T.M., Rescorla, L.A. Manual for the ASEBA adult forms & profiles an integrated system of multi-informant assessment reliability, internal consistency, cross-informant agreement, and stability. https://aseba.org/wp-content/uploads/2019/01/ASEBA-Reliability-and-Validity-Adult.pdf. Accessed November 14, 2019.
  • Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60(1), 693–716. https://doi.org/10.1146/annurev.psych.60.110707.163514
  • Arditi, A. (2004). Improving letter contrast sensitivity testing. Investigative Ophthalmology, 45(13), 4583–4583.
  • Avinun, R., Israel, S., Knodt, A.R., & AR, H. (2020). Little evidence for associations between the big five personality traits and variability in brain gray or white matter. Neuroimage, 220, 117092. https://doi.org/10.1016/J.NEUROIMAGE.2020.117092
  • Babakhanyan, I., McKenna, B.S., Casaletto, K.B., Nowinski, C.J., & Heaton, R.K. (2018). National institutes of health toolbox emotion battery for English- and Spanish-speaking adults: Normative data and factor-based summary scores. Patient Related Outcome Measures, 9, 115–127. https://doi.org/10.2147/prom.s151658
  • Baranger, D.A.A., Few, L.R., Sheinbein, D.H., Agrawal, A., Oltmanns, T. F., Knodt, A. R., Barch, D. M., Hariri, A. R., & Bogdan, R. (2020). Borderline personality traits are not correlated with brain structure in two large samples. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(7), 669–677. https://doi.org/10.1016/J.BPSC.2020.02.006
  • Barch, D.M., Burgess, G.C., Harms, M.P., Petersen, S.E., Schlaggar, B.L., Corbetta, M., . . . Nolan, D. (2013). Function in the human connectome: Task-fMRI and individual differences in behavior. Neuroimage, 80, 169–189. https://doi.org/10.1016/J.NEUROIMAGE.2013.05.033
  • Berk, M., & Berk, L. (2017). Cognition in psychiatric disorders: From models to management. The Lancet Psychiatry, 4(3), 173–175. https://doi.org/10.1016/S2215-0366(17)30040-8
  • Bierman, K.L., & Welsh, J.A. (2000). Assessing social dysfunction: The contributions of laboratory and performance-based measures. Journal of Clinical Child Psychology, 29(4), 526–539. https://doi.org/10.1207/S15374424JCCP2904_6
  • Blakemore, S.J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9(4), 267–277. https://doi.org/10.1038/nrn2353
  • Buysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., & Kupfer, D.J. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. https://doi.org/10.1016/0165-1781(89)90047-4
  • Cacioppo, J.T., Cacioppo, S., Dulawa, S., & Palmer, A.A. (2014). Social neuroscience and its potential contribution to psychiatry. World Psychiatry, 13(2), 131–139. https://doi.org/10.1002/wps.20118
  • Cacioppo, S., Capitanio, J.P., & Cacioppo, J.T. (2014). Toward a neurology of loneliness. Psychological Bulletin, 140(6), 1464–1504. https://doi.org/10.1037/a0037618
  • Cacioppo, J.T., Hughes, M.E., Waite, L.J., Hawkley, L.C., & Thisted, R.A. (2006). Loneliness as a specific risk factor for depressive symptoms: Cross-sectional and longitudinal analyses. Psychology and Aging, 21(1), 140–151. https://doi.org/10.1037/0882-7974.21.1.140
  • Cacioppo, J.T., Norris, C.J., Decety, J., Monteleone, G., & Nusbaum, H. (2009). In the eye of the beholder: Individual differences in perceived social isolation predict regional brain activation to social stimuli. Journal of Cognitive Neuroscience, 21(1), 83–92. https://doi.org/10.1162/jocn.2009.21007
  • Cearns, M., Hahn, T., & Baune, B.T. (2019). Recommendations and future directions for supervised machine learning in psychiatry. Translational Psychiatry, 9(1), 271. https://doi.org/10.1038/s41398-019-0607-2
  • Chou, K.-L., Liang, K., & Sareen, J. (2011). The ASsociation Between Social Isolation and DSM-IV mood, anxiety, and substance use disorders. The Journal of Clinical Psychiatry, 72(11), 1468–1476. https://doi.org/10.4088/JCP.10m06019gry
  • Chung, K., Wallace, J., Kim, S.Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., Mirzabekov, J. J., Zalocusky, K. A., Mattis, J., Denisin, A. K., Pak, S., Bernstein, H., Ramakrishnan, C., Grosenick, L., Gradinaru, V., & Deisseroth, K. (2013). Structural and molecular interrogation of intact biological systems. Nature, 497(7449), 332–337. https://doi.org/10.1038/nature12107
  • Cyranowski, J.M., Zill, N., Bode, R., Butt, Z., Kelly, M. A. R., Pilkonis, P. A., Salsman, J. M., & Cella, D. (2013). Assessing social support, companionship, and distress: National Institute of Health (NIH) toolbox adult social relationship scales. Health Psychology, 32(3), 293–301. https://doi.org/10.1037/a0028586
  • Dawson, E.L., Shear, P.K., & Strakowski, S.M. (2012). Behavior regulation and mood predict social functioning among healthy young adults. Journal of Clinical and Experimental Neuropsychology, 34(3), 297–305. https://doi.org/10.1080/13803395.2011.639297
  • de Nijs J, Burger, T.J., Janssen, R.J., Kia, S.M., van Opstal, D.P., de Koning, M.B., Schnack, . . . H.G. (2021). Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: A machine learning approach. Npj Schizophr, 7(1), 34. https://doi.org/10.1038/s41537-021-00162-3
  • Dunbar, R.I.M. (2009). The social brain hypothesis and its implications for social evolution. Annals of Human Biology, 36(5), 562–572. https://doi.org/10.1080/03014460902960289
  • Dunn, B.D., German, R.E., Khazanov, G., Xu, C., Hollon, S.D., & DeRubeis, R.J. (2020). Changes in positive and negative affect during pharmacological treatment and cognitive therapy for major depressive disorder: A secondary analysis of two randomized controlled trials. Clinical Psychological Science, 8(1), 36–51. https://doi.org/10.1177/2167702619863427
  • Dwyer, D.B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037
  • Elam, J. No title. HCP-YA data dictionary- updated for the 1200 subject release. https://wiki.humanconnectome.org/display/PublicData/HCP-YA+Data+Dictionary±Updated+for+the+1200+Subject+Release. Published 2021. Accessed January 12, 2020.
  • Estle, S.J., Green, L., Myerson, J., & Holt, D.D. (2006). Differential effects of amount on temporal and probability discounting of gains and losses. Memory & Cognition, 34(4), 914–928. http://www.ncbi.nlm.nih.gov/pubmed/17063921
  • Evans, V.C., Iverson, G.L., Yatham, L.N., & Lam, R.W. (2014). The relationship between neurocognitive and psychosocial functioning in major depressive disorder: A systematic review. The Journal of Clinical Psychiatry, 75(12), 1359–1370. https://doi.org/10.4088/JCP.13r08939
  • Fan, R.-E., Chang, K.W., Hsieh, C.J., Wang, X.R., & Lin, C.J. 2008.LIBLINEAR: A library for large linear classification. The Journal of machine Learning research, 9, 1871–1874. http://www.csie.ntu.edu.tw/ Accessed November 16, 2020
  • Fava, G.A., & Ruini, C. (2003). Development and characteristics of a well-being enhancing psychotherapeutic strategy: Well-being therapy. Journal of Behavior Therapy and Experimental Psychiatry, 34(1), 45–63. https://doi.org/10.1016/S0005-7916(03)00019-3
  • Feng, C., Wang, L., Li, T., & Xu, P. (2019). Connectome-based individualized prediction of loneliness. Social Cognitive and Affective Neuroscience, 14(4), 353–365. https://doi.org/10.1093/scan/nsz020
  • Fett, A.K.J., Viechtbauer, W., Dominguez, M. D. G., Penn, D.L., van Os, J., & Krabbendam, L. (2011). The relationship between neurocognition and social cognition with functional outcomes in schizophrenia: A meta-analysis. Neuroscience and Biobehavioral Reviews, 35(3), 573–588. https://doi.org/10.1016/j.neubiorev.2010.07.001
  • Fischer, A.H., & Manstead, A.S.R. (2016). Social functions of emotion and emotion regulation. In M. Lewis, J. Haviland-Jones, & L. Barrett (Eds.), Handbook of Emotions (4th ed., pp. 424–439). Guilford.
  • Folstein, M.F., Folstein, S.E., & Mchugh, P.R. (1975). “Mini-mENTAL STATE.” A PRACTICE METHOD FOR GRADING TEH COGNITIVE STATE OF PATIENTS FOR THE CLINIcian. Journal of psychiatric research, 12(3), 189–198.
  • Fratiglioni, L., Wang, H.-X., Ericsson, K., Maytan, M., & Winblad, B. (2000). Influence of social network on occurrence of dementia: A community-based longitudinal study. Lancet, 355(9212), 1315–1319. https://doi.org/10.1016/S0140-6736(00)02113-9
  • Fulford, D., Niendam, T.A., Floyd, E.G., Carter, C.S., Mathalon, D.H., Vinogradov, S., Stuart, B.K., & Loewy, R.L. (2013). Symptom dimensions and functional impairment in early psychosis: More to the story than just negative symptoms. Schizophrenia Research, 147(1), 125–131. https://doi.org/10.1016/j.schres.2013.03.024
  • Gallant, M.P. (2003). The influence of social support on chronic illness self-management: A review and directions for research. Health Education & Behavior : The Official Publication of the Society for Public Health Education, 30(2), 170–195. https://doi.org/10.1177/1090198102251030
  • Gitlin, M.J., & Miklowitz, D.J. (2017). The difficult lives of individuals with bipolar disorder: A review of functional outcomes and their implications for treatment. Journal of Affective Disorders, 209(November 2016), 147–154. https://doi.org/10.1016/j.jad.2016.11.021
  • Green, M.F., Horan, W.P., Lee, J., McCleery, A., Reddy, L.F., & Wynn, J.K. (2018). Social disconnection in schizophrenia and the general community. Schizophrenia Bulletin, 44(2), 242–249. https://doi.org/10.1093/schbul/sbx082
  • Green, M., Kern, R., Braff, D., & Mintz, J. (2000). Neurocognitive deficits and functional outcome in schizophrenia: are we measuring the “right stuff”? Schizophrenia Bulletin, 26(1), 119–136. https://doi.org/10.1093/oxfordjournals.schbul.a033430
  • Grove, T.B., Tso, I.F., Chun, J., Mueller, S. A., Taylor, S. F., Ellingrod, V. L., McInnis, M. G., & Deldin, P. J. (2016). Negative affect predicts social functioning across schizophrenia and bipolar disorder: Findings from an integrated data analysis. Psychiatry Research, 243, 198–206. https://doi.org/10.1016/J.PSYCHRES.2016.06.031
  • Gur, R.C., Sara, R., Hagendoorn, M., Marom, O., Hughett, P., Macy, L., Turner, T., Bajcsy, R., Posner, A., & Gur, R. E. (2002). A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies. Journal of Neuroscience Methods, 115(2), 137–143. https://doi.org/10.1016/s0165-0270(02)00006-7
  • Hahn, E.A., Cella, D., Chassany, O., Fairclough, D. L., Wong, G. Y., & Hays, R. D. (2007). Precision of health-related quality-of-life data compared with other clinical measures. Mayo Clinic Proceedings, 82(10), 1244–1254. https://doi.org/10.4065/82.10.1244
  • Hahn, T., Nierenberg, A., & Whitfield-Gabrieli, S. (2016). Predictive analytics in mental health: Applications, guidelines, challenges and perspectives. https://doi.org/10.1038/mp.2016.201
  • Haining, K., Brunner, G., Gajwani, R., Gross, J., Gumley, A. I., Lawrie, S. M., Schwannauer, M., Schultze-Lutter, F., & Uhlhaas, P. J. (2021). The relationship between cognitive deficits and impaired short-term functional outcome in clinical high-risk for psychosis participants: A machine learning and modelling approach. Schizophrenia Research, 231, 24–31. https://doi.org/10.1016/J.SCHRES.2021.02.019
  • Halverson, T.F., Orleans-Pobee, M., Merritt, C., Sheeran, P., Fett, A.-K., & Penn, D.L. (2019). Pathways to functional outcomes in schizophrenia spectrum disorders: Meta-analysis of social cognitive and neurocognitive predictors. https://doi.org/10.1016/j.neubiorev.2019.07.020
  • Hawkley, L.C., & Capitanio, J.P. (2015). Perceived social isolation, evolutionary fitness and health outcomes: A lifespan approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1669), 20140114. https://doi.org/10.1098/rstb.2014.0114
  • Hofmann, S.G., Asnaani, A., Vonk, I.J.J., Sawyer, A.T., & Fang, A. (2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive Therapy and Research, 36(5), 427–440. https://doi.org/10.1007/s10608-012-9476-1
  • Holt-Lunstad, J., Robles, T.F., & Sbarra, D.A. (2010). Julianne Holt-lunstad note. Advancing Social Connection as a Public Health Priority in the United States, 72(6), 517–530. https://doi.org/10.1037/amp0000103.supp
  • Holt-Lunstad, J., Smith, T.B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and social isolation as risk factors for mortality. Perspectives on Psychological Science : A Journal of the Association for Psychological Science, 10(2), 227–237. https://doi.org/10.1177/1745691614568352
  • Hooker, C.I., Gyurak, A., Verosky, S.C., Miyakawa, A., & Ayduk, Ö. (2010). Neural activity to a partner’s facial expression predicts self-regulation after conflict. Biological Psychiatry, 67(5), 406–413. https://doi.org/10.1016/j.biopsych.2009.10.014
  • Hooker, C.I., Verosky, S.C., Germine, L.T., Knight, R.T., & D’Esposito, M. (2010a). Neural activity during social signal perception correlates with self-reported empathy. Brain Research, 1308, 100–113. https://doi.org/10.1016/j.brainres.2009.10.006
  • Huppert, F.A. (2009). Psychological well-being: Evidence regarding its causes and consequences. Applied Psychology: Health and Well-Being, 1(2), 137–164. https://doi.org/10.1111/j.1758-0854.2009.01008.x
  • Inagaki, T.K., Muscatell, K.A., Moieni, M., Dutcher, J.M., Jevtic, I., Irwin, M.R., & Eisenberger, N.I (2016). Yearning for connection? Loneliness is associated with increased ventral striatum activity to close others. Social Cognitive and Affective Neuroscience, 11(7), 1096–1101. https://doi.org/10.1093/scan/nsv076
  • Iosifescu, D. V. (2012). The relation between mood, cognition and psychosocial functioning in psychiatric disorders. European Neuropsychopharmacology : The Journal of the European College of Neuropsychopharmacology, 22, S499–504. https://doi.org/10.1016/J.EURONEURO.2012.08.002
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (1st ed.). Springer. https://doi.org/10.1007/978-1-4614-7138-7
  • Janssens, M., Lataster, T., Simons, C.J.P., Oorschot, M., Lardinois, M., van Os, J., & Myin-Germeys, I. (2012). Emotion recognition in psychosis: No evidence for an association with real world social functioning. Schizophrenia Research, 142(1–3), 116–121. https://doi.org/10.1016/J.SCHRES.2012.10.003
  • Kambeitz-Ilankovic, L., Meisenzahl, E.M., Cabral, C., von Saldern, S., Kambeitz, J., Falkai, P., Möller, H.-J., Reiser, M., & Koutsouleris, N. (2016). Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophrenia Research, 173(3), 159–165. https://doi.org/10.1016/j.schres.2015.03.005
  • Keltner, D., & Kring, A. (1998). Emotion, social function, and psychopathology. Review of General Psychology : Journal of Division 1, of the American Psychological Association, 2(3), 320–342.
  • Kharabian Masouleh, S., Eickhoff, S.B., Hoffstaedter, F., & Genon, S. (2019). Alzheimer’s disease neuroimaging initiative ADN. empirical examination of the replicability of associations between brain structure and psychological variables. Elife, 8. https://doi.org/10.7554/eLife.43464
  • Koutsouleris, N., Kahn, R.S., Chekroud, A.M., Leucht, S., Falkai, P., Wobrock, T., Derks, E.M., Fleischhacker, & W.W., Hasan, A. (2016). Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: A machine learning approach. The Lancet Psychiatry, 3(10), 935–946. https://doi.org/10.1016/S2215-0366(16)30171-7
  • Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D.B., . . . Schmidt, A. (2018). Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression. JAMA Psychiatry, 75(11), 1156. https://doi.org/10.1001/jamapsychiatry.2018.2165
  • Lam, J.A., Murray, E.R., Yu, K.E., Ramsey, M., Nguyen, T.T., Mishra, J., Martis, B., Thomas, M.L., & Lee, E.E. (2021). Neurobiology of loneliness: A systematic review. Neuropsychopharmacology, 46(11), 1873–1887. https://doi.org/10.1038/s41386-021-01058-7
  • Leighton, S.P., Krishnadas, R., Chung, K., Blair, A., Brown, S., Clark, S., Sowerbutts, K., . . . Gumley, A. (2019). Predicting one-year outcome in first episode psychosis using machine learning. PLoS One. Acampora G, ed, 14(3), e0212846. https://doi.org/10.1371/journal.pone.0212846
  • Leighton, S.P., Upthegrove, R., Krishnadas, R., Benros, M. E., Broome, M. R., Gkoutos, G. V., Liddle, P. F., Singh, S. P., Everard, L., Jones, P. B., Fowler, D., Sharma, V., Freemantle, N., Christensen, R. H. B., Albert, N., Nordentoft, M., Schwannauer, M., Cavanagh, J., Gumley, A. I. … Mallikarjun, P. K. (2019). Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: A machine learning approach. The Lancet Digital Health, 1(6), e261–270. https://doi.org/10.1016/S2589-7500(19)30121-9
  • McCrae, R.R., & Costa, P.T. (2004). A contemplated revision of the NEO five-factor inventory. Personality and Individual Differences, 36(3), 587–596. https://doi.org/10.1016/S0191-8869(03)00118-1
  • Millan, M.J., Agid, Y., Brüne, M., Bullmore, E. T., Carter, C. S., Clayton, N. S., Connor, R., Davis, S., Deakin, B., DeRubeis, R. J., Dubois, B., Geyer, M. A., Goodwin, G. M., Gorwood, P., Jay, T. M., Joëls, M., Mansuy, I. M., Meyer-Lindenberg, A., Murphy, D. … Young, L. J. (2012). Cognitive dysfunction in psychiatric disorders: Characteristics, causes and the quest for improved therapy. Nature Reviews Drug Discovery, 11(2), 141–168. https://doi.org/10.1038/nrd3628
  • Moffitt, T.E. (2018). Male antisocial behaviour in adolescence and beyond. Nature Human Behaviour, 2(3), 177–186. https://doi.org/10.1038/s41562-018-0309-4
  • Moffitt, T.E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., Houts, R., Poulton, R., Roberts, B. W., Ross, S., Sears, M. R., Thomson, W. M., & Caspi, A. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences, 108(7), 2693–2698. https://doi.org/10.1073/pnas.1010076108
  • Moradi, E., Khundrakpam, B., Lewis, J.D., Evans, A.C., & Tohka, J. (2017). Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data. Neuroimage, 144, 128–141. https://doi.org/10.1016/j.neuroimage.2016.09.049
  • Murphy, B.C., Shepard, S.A., Eisenberg, N., & Fabes, R.A. (2004). Concurrent and across time prediction of young adolescents’ social functioning: The role of emotionality and regulation. Social Development, 13(1), 56–86. https://doi.org/10.1111/j.1467-9507.2004.00257.x
  • Obermeyer, Z., & Emanuel, E.J. (2016). Predicting the future — big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
  • Oliver, L.D., Haltigan, J.D., Gold, J.M., Foussias, G., DeRosse, P., Buchanan, R., Malhotra, A., & Voineskos, A. (2018). Lower- and higher-level social cognitive factors across individuals with schizophrenia spectrum disorders and healthy controls. Relationship with Neurocognition and Functional Outcome, 44(suppl_1), 1–10. https://doi.org/10.1093/schbul/sby114
  • Orphanidou, C., & Wong, D. (2017). Machine learning models for multidimensional clinical data. Springer. https://doi.org/10.1007/978-3-319-58280-1_8
  • Pantell, M., Rehkopf, D., Jutte, D., Syme, S.L., Balmes, J., & Adler, N. (2013). Social isolation: A predictor of mortality comparable to traditional clinical risk factors. American Journal of Public Health, 103(11), 2056–2062. https://doi.org/10.2105/AJPH.2013.301261
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., and Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
  • Pinkham, A.E. (2014). Social cognition in schizophrenia. The Journal of Clinical Psychiatry, 75(SUPPL. 2), 14–19. https://doi.org/10.4088/JCP.13065su1.04
  • Reniers, R.L., Lin, A., Yung, A.R., Koutsouleris, N., Nelson, B., Cropley, V. L., Velakoulis, D., McGorry, P. D., Pantelis, C., & Wood, S. J. (2017). Neuroanatomical predictors of functional outcome in individuals at ultra-high risk for psychosis. Schizophrenia Bulletin, 43(2), 449–458. https://doi.org/10.1093/schbul/sbw086
  • Reuben, D.B., Magasi, S., McCreath, H.E., Bohannon, R. W., Wang, Y.-C., Bubela, D. J., Rymer, W. Z., Beaumont, J., Rine, R. M., Lai, J.-S., & Gershon, R. C. (2013). Motor assessment using the NIH toolbox. Neurology, 80(11 Suppl 3), S65–75. https://doi.org/10.1212/WNL.0b013e3182872e01
  • Riley, W.T., Pilkonis, P., & Cella, D. (2011). Application of the national institutes of health patient-reported outcome measurement information system (PROMIS) to mental health research. The Journal of Mental Health Policy and Economics, 14(4), 201–208.
  • Ryff, C.D. (2014). Psychological well-being revisited: Advances in the science and practice of eudaimonia. Psychotherapy and Psychosomatics, 83(1), 10–28. https://doi.org/10.1159/000353263
  • Salsman, J.M., Butt, Z., Pilkonis, P.A., Cyranowski, J. M., Zill, N., Hendrie, H. C., Kupst, M. J., Kelly, M. A. R., Bode, R. K., Choi, S. W., Lai, J.-S., Griffith, J. W., Stoney, C. M., Brouwers, P., Knox, S. S., & Cella, D. (2013). Emotion assessment using the NIH toolbox. Neurology, 80(Issue 11, Supplement 3), S76–86. https://doi.org/10.1212/WNL.0b013e3182872e11
  • Sanfelici, R., Dwyer, D.B., Antonucci, L.A., & Koutsouleris, N. (2020). Individualized diagnostic and prognostic models for patients with psychosis risk syndromes: a meta-analytic view on the state of the art. Biological Psychiatry, 88(4), 349–360. https://doi.org/10.1016/J.BIOPSYCH.2020.02.009
  • Sanmartin, R., Ingles, C., Vicent, M., Gonzalvez, C., Diaz-Herrero, A., Garcia-Fernandez, J., & Lin, C.-Y. (2018). Positive and negative affect as predictors of social functioning in Spanish children. PLoS One, 13(8), e0201698. https://doi.org/10.1371/journal.pone.0201698
  • Santini, Z.I., Fiori, K.L., Feeney, J., Tyrovolas, S., Haro, J.M., & Koyanagi, A. (2016). Social relationships, loneliness, and mental health among older men and women in Ireland: A prospective community-based study. Journal of Affective Disorders, 204, 59–69. https://doi.org/10.1016/j.jad.2016.06.032
  • Santini, Z.I., Jose, P.E., York Cornwell, E., Koyanagi, A., Nielsen, L., Hinrichsen, C., Meilstrup, C., . . . Koushede, V. (2020). Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): A longitudinal mediation analysis. Lancet Public Health, 5(1), e62–70. https://doi.org/10.1016/S2468-2667(19)30230-0
  • Santini, Z.I., Koyanagi, A., Tyrovolas, S., Mason, C., & Haro, J.M. (2015). The association between social relationships and depression: A systematic review. Journal of Affective Disorders, 175, 53–65. https://doi.org/10.1016/J.JAD.2014.12.049
  • Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random forest algorithm for the classification of neuroimaging data in alzheimer’s disease: A systematic review. Frontiers in Aging Neuroscience, 9, 329. https://doi.org/10.3389/fnagi.2017.00329
  • Sartori, J.M., Reckziegel, R., Passos, I.C., Czepielewski, L. S., Fijtman, A., Sodré, L. A., Massuda, R., Goi, P. D., Vianna-Sulzbach, M., Cardoso, T. D. A., Kapczinski, F., Mwangi, B., & Gama, C. S. (2018). Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach. Journal of Psychiatric Research, 103(April), 237–243. https://doi.org/10.1016/j.jpsychires.2018.05.023
  • Schnack, H.G. (2019). Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases). Schizophrenia Research, 214, 34–42. https://doi.org/10.1016/J.SCHRES.2017.10.023
  • Sen, C. P., Saucier, D.A., & Hafner, E. (2010). Meta-analysis of the relationships between social support and well-being in children and adolescents. Journal of Social and Clinical Psychology, 29(6), 624–645. https://doi.org/10.1521/jscp.2010.29.6.624
  • Shankar, A., Hamer, M., McMunn, A., & Steptoe, A. (2013). Social isolation and loneliness. Psychosomatic Medicine, 75(2), 161–170. https://doi.org/10.1097/PSY.0b013e31827f09cd
  • Siedlecki, K.L., Salthouse, T.A., Oishi, S., & Jeswani, S. (2014). The relationship between social support and subjective well-being across age. Social Indicators Research, 117(2), 561–576. https://doi.org/10.1007/s11205-013-0361-4
  • Smith, A.W., Sa K, M., De Aguiar, C., Moy, C., Riley, W. T., Wagster, M. V. M., & Werner, E. (2016). News from the NIH: Person-centered outcomes measurement: NIH-supported measurement systems to evaluate self-assessed health, functional performance, and symptomatic toxicity. Translational Behavioral Medicine, 6(3), 470–474. https://doi.org/10.1007/s13142-015-0345-9
  • Teo, A.R., Lerrigo, R., & Rogers, M.A.M. (2013). The role of social isolation in social anxiety disorder: A systematic review and meta-analysis. Journal of Anxiety Disorders, 27(4), 353–364. https://doi.org/10.1016/J.JANXDIS.2013.03.010
  • Valtorta, N.K., Kanaan, M., Gilbody, S., Ronzi, S., & Hanratty, B. (2016). Loneliness and social isolation as risk factors for coronary heart disease and stroke: Systematic review and meta-analysis of longitudinal observational studies. Heart, 102(13), 1009–1016. https://doi.org/10.1136/heartjnl-2015-308790
  • Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn human connectome project. An Overview, 71(2), 233–236. https://doi.org/10.1038/mp.2011.182
  • Van Overwalle, F. (2009). Social cognition and the brain: A meta-analysis. Human Brain Mapping, 30(3), 829–858. https://doi.org/10.1002/hbm.20547
  • Vassilev, I., Rogers, A., Sanders, C., Kennedy, A., Blickem, C., Protheroe, J., Bower, P., Kirk, S., Chew-Graham, C., & Morris, R. (2011). Social networks, social capital and chronic illness self-management: A realist review. Chronic Illness, 7(1), 60–86. https://doi.org/10.1177/1742395310383338
  • Vieira, S., Gong, Q., Pinaya, W.H.L., Scarpazza, C., Tognin, S., Crespo-Facorro, B., Tordesillas-Gutierrez, D., Ortiz-García, V., Setien-Suero, E., Scheepers, F. E., Van Haren, N. E. M., Marques, T. R., Murray, R. M., David, A., Dazzan, P., McGuire, P., & Mechelli, A. (2020). Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence. Schizophrenia Bulletin, 46(1), 17–26. https://doi.org/10.1093/schbul/sby189
  • Weiss, L.A., Westerhof, G.J., & Bohlmeijer, E.T. (2016). Can we increase psychological well-being? The effects of interventions on psychological well-being: A meta-analysis of randomized controlled trials. PLoS One, 11(6), e0158092. Coyne J, ed. https://doi.org/10.1371/journal.pone.0158092
  • Wojtalik, J.A., Smith, M.J., Keshavan, M.S., & Eack, S.M. (2018). A systematic And meta-analytic review of neural correlates of functional outcome in schizophrenia. Schizophrenia Bulletin. 43(6), 1329–1347. https://doi.org/10.1093/schbul/sbx008
  • Yager, J.A., Ehmann, T.S. Untangling Social Function Yager and Ehmann Untangling Social Function and Social Cognition: A Review of Concepts and Measurement. http://do.org/guilfordjournals.com/do/pdfplus/10.1521/psyc.2006.69.1.47?casa_token=M-DsnBFTwDEAAAAA:1hF-Q3n88EtjqpjWRa0iZS8auOjzbM6Uu-65dgbfgnNkyLpQFzK_eaYxJom64zno9zEv6t52m4zB. Accessed October 7, 2020.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.