1932

Abstract

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-101220-014053
2021-07-08
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/neuro/44/1/annurev-neuro-101220-014053.html?itemId=/content/journals/10.1146/annurev-neuro-101220-014053&mimeType=html&fmt=ahah

Literature Cited

  1. Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T. 2016. Automatic detection of social rhythms in bipolar disorder. J. Am. Med. Inform. Assoc. 23:3538–43
    [Google Scholar]
  2. Abdullah S, Matthews M, Murnane EL, Gay G, Choudhury T. 2014. Towards circadian computing: “Early to bed and early to rise” makes some of us unhealthy and sleep deprived Paper presented at UBICOMP Seattle, WA:
  3. APA (Am. Psychiatr. Assoc.) 2013. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 Washington, DC: Am. Psychiatr. Publ, 5th ed..
  4. Bäckman L, Nyberg L, Lindenberger U, Li SC, Farde L. 2006. The correlative triad among aging, dopamine, and cognition: current status and future prospects. Neurosci. Biobehav. Rev. 30:6791–807
    [Google Scholar]
  5. Baker JT, Dillon DG, Patrick LM, Roffman JL, Brady RO et al. 2019. Functional connectomics of affective and psychotic pathology. PNAS 116:189050–59
    [Google Scholar]
  6. Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela JP. 2018. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 43:81660–66
    [Google Scholar]
  7. Bedder R, Vaghi MM, Dolan RJ, Rutledge R. 2020. Risk taking for potential losses but not gains increases with time of day. PsyArXiv. https://doi.org/10.31234/osf.io/3qdnx
    [Crossref]
  8. Ben-Zeev D, Brian R, Wang R, Wang W, Campbell AT et al. 2017. CrossCheck: integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse. Psychiatr. Rehabil. J. 40:3266–75
    [Google Scholar]
  9. Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. 2015. Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health. Psychiatr. Rehabil. J. 38:3218–26
    [Google Scholar]
  10. Bickel WK, Jarmolowicz DP, Mueller ET, Koffarnus MN, Gatchalian KM. 2012. Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other disease-related vulnerabilities: emerging evidence. Pharmacol. Ther. 134:3287–97
    [Google Scholar]
  11. Blain B, Rutledge RB. 2020. Momentary subjective well-being depends on learning and not reward. eLife 9:e57977
    [Google Scholar]
  12. Blum S, Debener S, Emkes R, Volkening N, Fudickar S, Bleichner MG. 2017. EEG recording and online signal processing on Android: a multiapp framework for brain-computer interfaces on smartphone. Biomed. Res. Int. 2017.3072870
    [Google Scholar]
  13. Braithwaite SR, Giraud-Carrier C, West J, Barnes MD, Hanson CL. 2016. Validating machine learning algorithms for Twitter data against established measures of suicidality. JMIR Ment. Health 3:2e21
    [Google Scholar]
  14. Brown HR, Zeidman P, Smittenaar P, Adams RA, McNab F et al. 2014. Crowdsourcing for cognitive science—the utility of smartphones. PLOS ONE 9:7e100662
    [Google Scholar]
  15. Browning M, Carter C, Chatham C, Den Ouden H, Gillan CM et al. 2020. Realising the clinical potential of computational psychiatry: report from the Banbury Centre meeting, February 2019. Biol. Psychiatry 88:2e5–10
    [Google Scholar]
  16. Bucci W, Freedman N. 1981. The language of depression. Bull. Menninger Clin. 45:4334–58
    [Google Scholar]
  17. Buhrmester M, Kwang T, Gosling SD. 2011. Amazon's Mechanical Turk: a new source of inexpensive, yet high-quality, data?. Perspect. Psychol. Sci. 6:13–5
    [Google Scholar]
  18. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J et al. 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14:5365–76
    [Google Scholar]
  19. Caliskan A, Bryson JJ, Narayanan A. 2017. Semantics derived automatically from language corpora contain human-like biases. Science 356:6334183–86
    [Google Scholar]
  20. Canzian L, Musolesi M. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis Paper presented at UBICOMP Osaka, Japan:
  21. Casler K, Bickel L, Hackett E. 2013. Separate but equal? A comparison of participants and data gathered via Amazon's MTurk, social media, and face-to-face behavioral testing. Comput. Hum. Behav. 29:62156–60
    [Google Scholar]
  22. Chandler J, Paolacci G. 2017. Lie for a dime: when most prescreening responses are honest but most study participants are impostors. Soc. Psychol. Personal. Sci. 8:5500–8
    [Google Scholar]
  23. Chandler J, Shapiro D. 2016. Conducting clinical research using crowdsourced convenience samples. Annu. Rev. Clin. Psychol. 12:53–81
    [Google Scholar]
  24. Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. 2017. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry 74:4370–78
    [Google Scholar]
  25. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK et al. 2016. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3:3243–50
    [Google Scholar]
  26. Chekroud SR, Gueorguieva R, Zheutlin AB, Paulus M, Krumholz HM et al. 2018. Association between physical exercise and mental health in 1.2 million individuals in the USA between 2011 and 2015: a cross-sectional study. Lancet Psychiatry 5:9739–46
    [Google Scholar]
  27. Chen X, Rutledge RB, Brown HR, Dolan RJ, Bestmann S, Galea JM. 2018. Age-dependent Pavlovian biases influence motor decision-making. PLOS Comput. Biol. 14:7e1006304
    [Google Scholar]
  28. Chew B, Hauser TU, Papoutsi M, Magerkurth J, Dolan RJ, Rutledge RB 2019. Endogenous fluctuations in the dopaminergic midbrain drive behavioral choice variability. PNAS 116:3718732–37
    [Google Scholar]
  29. Chien I, Enrique A, Palacios J, Regan T, Keegan D et al. 2020. A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions. JAMA Netw. Open 3:7e2010791
    [Google Scholar]
  30. Clarke DE, Narrow WE, Regier DA, Kuramoto SJ, Kupfer DJ et al. 2013. DSM-5 field trials in the United States and Canada, Part I: study design, sampling strategy, implementation, and analytic approaches. Am. J. Psychiatry 170:143–58
    [Google Scholar]
  31. Cohen AS, Cowan T, Le TP, Schwartz EK, Kirkpatrick B et al. 2020. Ambulatory digital phenotyping of blunted affect and alogia using objective facial and vocal analysis: proof of concept. Schizophr. Res. 220:141–46
    [Google Scholar]
  32. Cohen AS, Mitchell KR, Elvevåg B. 2014. What do we really know about blunted vocal affect and alogia? A meta-analysis of objective assessments. Schizophr. Res. 159:2–3533–38
    [Google Scholar]
  33. Cornet VP, Holden RJ. 2018. Systematic review of smartphone-based passive sensing for health and wellbeing. J. Biomed. Inform. 77:120–32
    [Google Scholar]
  34. Coughlan G, Coutrot A, Khondoker M, Minihane AM, Spiers H, Hornberger M 2019. Toward personalized cognitive diagnostics of at-genetic-risk Alzheimer's disease. PNAS 116:199285–92
    [Google Scholar]
  35. Coutrot A, Silva R, Manley E, de Cothi W, Sami S et al. 2018. Global determinants of navigation ability. Curr. Biol. 28:172861–66.e4
    [Google Scholar]
  36. Crump MJ, McDonnell JV, Gureckis TM. 2013. Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research. PLOS ONE 8:3e57410
    [Google Scholar]
  37. Daniel-Watanabe L, McLaughlin M, Gormley S, Robinson OJ. 2020. Association between a directly translated cognitive measure of negative bias and self-reported psychiatric symptoms. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. In press
    [Google Scholar]
  38. Davidson LL, Heinrichs RW. 2003. Quantification of frontal and temporal lobe brain-imaging findings in schizophrenia: a meta-analysis. Psychiatry Res 122:269–87
    [Google Scholar]
  39. Daw ND, Gershman SJ, Seymour B, Dayan P, Dolan RJ. 2011. Model-based influences on humans' choices and striatal prediction errors. Neuron 69:61204–15
    [Google Scholar]
  40. Dayan P, Huys QJ. 2008. Serotonin, inhibition, and negative mood. PLOS Comput. Biol. 4:2e4
    [Google Scholar]
  41. De Choudhury M, Gamon M, Counts S, Horvitz E. 2013. Predicting depression via social media Paper presented at the 7th International AAAI Conference on Weblogs and Social Media Cambridge, MA:
  42. Eldar E, Roth C, Dayan P, Dolan RJ. 2018. Decodability of reward learning signals predicts mood fluctuations. Curr. Biol. 28:91433–39.e7
    [Google Scholar]
  43. Farrell MS, Werge T, Sklar P, Owen MJ, Ophoff RA et al. 2015. Evaluating historical candidate genes for schizophrenia. Mol. Psychiatry 20:5555–62
    [Google Scholar]
  44. Flint J, Kendler KS. 2014. The genetics of major depression. Neuron 81:3484–503
    [Google Scholar]
  45. Fluharty M, Taylor AE, Grabski M, Munafò MR. 2017. The association of cigarette smoking with depression and anxiety: a systematic review. Nicotine Tob. Res. 19:13–13
    [Google Scholar]
  46. Ford DE, Kamerow DB. 1989. Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention?. JAMA 262:111479–84
    [Google Scholar]
  47. Fried EI, Nesse RM. 2015. Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. J. Affect. Disord. 172:96–102
    [Google Scholar]
  48. Fuller PM, Gooley JJ, Saper CB. 2006. Neurobiology of the sleep-wake cycle: sleep architecture, circadian regulation, and regulatory feedback. J. Biol. Rhythms 21:482–93
    [Google Scholar]
  49. Germine L, Nakayama K, Duchaine BC, Chabris CF, Chatterjee G, Wilmer JB. 2012. Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments. Psychon. Bull. Rev. 19:5847–57
    [Google Scholar]
  50. Gillan CM, Daw ND. 2016. Taking psychiatry research online. Neuron 91:119–23
    [Google Scholar]
  51. Gillan CM, Fineberg NA, Robbins TW. 2017. A trans-diagnostic perspective on obsessive-compulsive disorder. Psychol. Med. 47:91528–48
    [Google Scholar]
  52. Gillan CM, Kalanthroff E, Evans M, Weingarden HM, Jacoby RJ et al. 2019. Comparison of the association between goal-directed planning and self-reported compulsivity versus obsessive-compulsive disorder diagnosis. JAMA Psychiatry 77:11–10
    [Google Scholar]
  53. Gillan CM, Kosinski M, Whelan R, Phelps EA, Daw ND. 2016. Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. eLife 5:e11305
    [Google Scholar]
  54. Gillan CM, Seow TXF. 2020. Carving out new transdiagnostic dimensions for research in mental health. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5:10932–34
    [Google Scholar]
  55. Gillan CM, Whelan R. 2017. What big data can do for treatment in psychiatry. Curr. Opin. Behav. Sci. 18:34–42
    [Google Scholar]
  56. Goel N, Rao H, Durmer JS, Dinges DF. 2009. Neurocognitive consequences of sleep deprivation. Semin. Neurol. 29:4320–39
    [Google Scholar]
  57. Goldman N. 1994. Social factors and health: the causation-selection issue revisited. PNAS 91:41251–55
    [Google Scholar]
  58. Goodman JK, Cryder CE, Cheema A. 2013. Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples. J. Behav. Decis. Making 26:213–24
    [Google Scholar]
  59. Grosenick L, Shi TC, Gunning FM, Dubin MJ, Downar J, Liston C. 2019. Functional and optogenetic approaches to discovering stable subtype-specific circuit mechanisms in depression. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4:6554–66
    [Google Scholar]
  60. Harari GM, Müller SR, Aung MS, Rentfrow P. 2017. Smartphone sensing methods for studying behavior in everyday life. Curr. Opin. Behav. Sci. 18:83–90
    [Google Scholar]
  61. Haslam N, Holland E, Kuppens P. 2012. Categories versus dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychol. Med. 42:5903–20
    [Google Scholar]
  62. Haworth CM, Harlaar N, Kovas Y, Davis OS, Oliver BR et al. 2007. Internet cognitive testing of large samples needed in genetic research. Twin Res. Hum. Genet. 10:4554–63
    [Google Scholar]
  63. Hedge C, Powell G, Sumner P. 2018. The reliability paradox: why robust cognitive tasks do not produce reliable individual differences. Behav. Res. Methods 50:31166–86
    [Google Scholar]
  64. Heller AS, Shi TC, Ezie CEC, Reneau TR, Baez LM et al. 2020. Association between real-world experiential diversity and positive affect relates to hippocampal-striatal functional connectivity. Nat. Neurosci. 23:7800–4
    [Google Scholar]
  65. Henrich J, Heine SJ, Norenzayan A. 2010. The weirdest people in the world?. Behav. Brain Sci. 33:2–361–83
    [Google Scholar]
  66. Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M et al. 2019. Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples. Am. J. Psychiatry 176:7531–42
    [Google Scholar]
  67. Hunt LT, Rutledge RB, Malalasekera WM, Kennerley SW, Dolan RJ. 2016. Approach-induced biases in human information sampling. PLOS Biol 14:11e2000638
    [Google Scholar]
  68. Hunter LE, Meer EA, Gillan CM, Hsu M, Daw ND. 2019. Excessive deliberation in social anxiety. bioRxiv 522433. https://doi.org/10.1101/522433
    [Crossref]
  69. Huys QJ, Maia TV, Frank MJ. 2016. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19:3404–13
    [Google Scholar]
  70. Insel TR. 2018. Digital phenotyping: a global tool for psychiatry. World Psychiatry 17:3276–77
    [Google Scholar]
  71. Jané-Llopis E, Matytsina I 2006. Mental health and alcohol, drugs and tobacco: a review of the comorbidity between mental disorders and the use of alcohol, tobacco and illicit drugs. Drug Alcohol Rev 25:6515–36
    [Google Scholar]
  72. Jarvis MJ. 1993. Does caffeine intake enhance absolute levels of cognitive performance?. Psychopharmacology 110:1–245–52
    [Google Scholar]
  73. Kalaria RN, Maestre GE, Arizaga R, Friedland RP, Galasko D et al. 2008. Alzheimer's disease and vascular dementia in developing countries: prevalence, management, and risk factors. Lancet Neurol 7:9812–26
    [Google Scholar]
  74. Kapur S, Phillips AG, Insel TR. 2012. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?. Mol. Psychiatry 17:121174–79
    [Google Scholar]
  75. Kelley S, Gillan C. 2020. Within-subject changes in network connectivity occur during an episode of depression: evidence from a longitudinal analysis of social media posts. PsyArXiv. https://doi.org/10.31234/osf.io/6h52d
    [Crossref]
  76. Kelly JR, Kennedy PJ, Cryan JF, Dinan TG, Clarke G, Hyland NP. 2015. Breaking down the barriers: the gut microbiome, intestinal permeability and stress-related psychiatric disorders. Front. Cell Neurosci. 9:392
    [Google Scholar]
  77. Kessler RC, Davis CG, Kendler KS. 1997. Childhood adversity and adult psychiatric disorder in the US National Comorbidity Survey. Psychol. Med. 27:51101–19
    [Google Scholar]
  78. Killingsworth MA, Gilbert DT. 2010. A wandering mind is an unhappy mind. Science 330:6006932
    [Google Scholar]
  79. Lathia N, Sandstrom GM, Mascolo C, Rentfrow PJ. 2017. Happier people live more active lives: using smartphones to link happiness and physical activity. PLOS ONE 12:1e0160589
    [Google Scholar]
  80. Lau-Zhu A, Lau MPH, McLoughlin G. 2019. Mobile EEG in research on neurodevelopmental disorders: opportunities and challenges. Dev. Cogn. Neurosci. 36:100635
    [Google Scholar]
  81. Lieberman HR, Tharion WJ, Shukitt-Hale B, Speckman KL, Tulley R. 2002. Effects of caffeine, sleep loss, and stress on cognitive performance and mood during U.S. Navy SEAL training. Sea-Air-Land. Psychopharmacology 164:3250–61
    [Google Scholar]
  82. Linn MC, Petersen AC. 1985. Emergence and characterization of sex differences in spatial ability: a meta-analysis. Child Dev 56:61479–98
    [Google Scholar]
  83. Lipszyc J, Schachar R. 2010. Inhibitory control and psychopathology: a meta-analysis of studies using the stop signal task. J. Int. Neuropsychol. Soc. 16:61064–76
    [Google Scholar]
  84. Lorant V, Deliège D, Eaton W, Robert A, Philippot P, Ansseau M. 2003. Socioeconomic inequalities in depression: a meta-analysis. Am. J. Epidemiol. 157:298–112
    [Google Scholar]
  85. Lyall LM, Wyse CA, Graham N, Ferguson A, Lyall DM et al. 2018. Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank. Lancet Psychiatry 5:6507–14
    [Google Scholar]
  86. MacKerron G, Mourato A. 2013. Happiness is greater in natural environments. Glob. Environ. Change 23:5992–1000
    [Google Scholar]
  87. Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP et al. 2020. Towards reproducible brain-wide association studies. bioRxiv 2020.08.21.257758. https://doi.org/10.1101/2020.08.21.257758
    [Crossref]
  88. McNab F, Zeidman P, Rutledge RB, Smittenaar P, Brown HR et al. 2015. Age-related changes in working memory and the ability to ignore distraction. PNAS 112:206515–18
    [Google Scholar]
  89. Michely J, Eldar E, Martin IM, Dolan RJ. 2020. A mechanistic account of serotonin's impact on mood. Nat. Commun. 11:12335
    [Google Scholar]
  90. Mohr DC, Zhang M, Schueller SM. 2017. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu. Rev. Clin. Psychol. 13:23–47
    [Google Scholar]
  91. Mota NB, Vasconcelos NA, Lemos N, Pieretti AC, Kinouchi O et al. 2012. Speech graphs provide a quantitative measure of thought disorder in psychosis. PLOS ONE 7:4e34928
    [Google Scholar]
  92. Müller VI, Cieslik EC, Serbanescu I, Laird AR, Fox PT, Eickhoff SB. 2017. Altered brain activity in unipolar depression revisited: meta-analyses of neuroimaging studies. JAMA Psychiatry 74:147–55
    [Google Scholar]
  93. Nussenbaum K, Scheuplein M, Phaneuf CV, Evans MD, Hartley CA. 2020. Moving developmental research online: comparing in-lab and web-based studies of model-based reinforcement learning. OSF Preprints. https://doi.org/10.1525/collabra.17213
    [Crossref]
  94. O'Neil A, Quirk SE, Housden S, Brennan SL, Williams LJ et al. 2014. Relationship between diet and mental health in children and adolescents: a systematic review. Am. J. Public Health 104:10e31–42
    [Google Scholar]
  95. Orban C, Kong R, Li J, Chee MW, Yeo BT. 2020. Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity. PLOS Biol 18:2e3000602
    [Google Scholar]
  96. Parkes L, Tiego J, Aquino K, Braganza L, Chamberlain SR et al. 2019. Transdiagnostic variations in impulsivity and compulsivity in obsessive-compulsive disorder and gambling disorder correlate with effective connectivity in cortical-striatal-thalamic-cortical circuits. Neuroimage 202:116070
    [Google Scholar]
  97. Paykel ES, Abbott R, Jenkins R, Brugha TS, Meltzer H. 2000. Urban-rural mental health differences in Great Britain: findings from the National Morbidity Survey. Psychol. Med. 30:2269–80
    [Google Scholar]
  98. Pennebaker JW, Mehl MR, Niederhoffer KG. 2003. Psychological aspects of natural language use: our words, our selves. Annu. Rev. Psychol. 54:547–77
    [Google Scholar]
  99. Pietrzak RH, Snyder PJ, Jackson CE, Olver J, Norman T et al. 2009. Stability of cognitive impairment in chronic schizophrenia over brief and intermediate re-test intervals. Hum. Psychopharmacol. 24:2113–21
    [Google Scholar]
  100. Pinto Pereira SM, Geoffroy MC, Power C 2014. Depressive symptoms and physical activity during 3 decades in adult life: bidirectional associations in a prospective cohort study. JAMA Psychiatry 71:121373–80
    [Google Scholar]
  101. Pooseh S, Bernhardt N, Guevara A, Huys QJM, Smolka MN. 2018. Value-based decision-making battery: a Bayesian adaptive approach to assess impulsive and risky behavior. Behav. Res. Methods 50:1236–49
    [Google Scholar]
  102. Pringle A, Browning M, Cowen PJ, Harmer CJ. 2011. A cognitive neuropsychological model of antidepressant drug action. Prog. Neuropsychopharmacol. Biol. Psychiatry 35:71586–92
    [Google Scholar]
  103. Radüntz T. 2018. Signal quality evaluation of emerging EEG devices. Front. Physiol. 9:98
    [Google Scholar]
  104. Reece AG, Reagan AJ, Lix KLM, Dodds PS, Danforth CM, Langer EJ. 2017. Forecasting the onset and course of mental illness with Twitter data. Sci. Rep. 7:113006
    [Google Scholar]
  105. Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ et al. 2013. DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. Am. J. Psychiatry 170:159–70
    [Google Scholar]
  106. Richard A, Rohrmann S, Vandeleur CL, Schmid M, Barth J, Eichholzer M. 2017. Loneliness is adversely associated with physical and mental health and lifestyle factors: results from a Swiss national survey. PLOS ONE 12:7e0181442
    [Google Scholar]
  107. Rigoli F, Rutledge RB, Dayan P, Dolan RJ. 2016. The influence of contextual reward statistics on risk preference. Neuroimage 128:74–84
    [Google Scholar]
  108. Rouault M, Seow T, Gillan CM, Fleming SM. 2018. Psychiatric symptom dimensions are associated with dissociable shifts in metacognition but not task performance. Biol. Psychiatry 84:6443–51
    [Google Scholar]
  109. Rude S, Gortner EM, Pennebaker J. 2004. Language use of depressed and depression-vulnerable college students. Cogn. Emot. 18:81121–33
    [Google Scholar]
  110. Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW et al. 2006. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163:111905–17
    [Google Scholar]
  111. Rutledge RB, Chekroud AM, Huys QJ. 2019. Machine learning and big data in psychiatry: toward clinical applications. Curr. Opin. Neurobiol. 55:152–59
    [Google Scholar]
  112. Rutledge RB, Moutoussis M, Smittenaar P, Zeidman P, Taylor T et al. 2017. Association of neural and emotional impacts of reward prediction errors with major depression. JAMA Psychiatry 74:8790–97
    [Google Scholar]
  113. Rutledge RB, Skandali N, Dayan P, Dolan RJ 2014. A computational and neural model of momentary subjective well-being. PNAS 111:3312252–57
    [Google Scholar]
  114. Rutledge RB, Skandali N, Dayan P, Dolan RJ. 2015. Dopaminergic modulation of decision making and subjective well-being. J. Neurosci. 35:279811–22
    [Google Scholar]
  115. Rutledge RB, Smittenaar P, Zeidman P, Brown HR, Adams RA et al. 2016. Risk taking for potential reward decreases across the lifespan. Curr. Biol. 26:121634–39
    [Google Scholar]
  116. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME et al. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res. 17:7e175
    [Google Scholar]
  117. Samanez-Larkin GR, Knutson B. 2015. Decision making in the ageing brain: changes in affective and motivational circuits. Nat. Rev. Neurosci. 16:5278–89
    [Google Scholar]
  118. Scollon CN, Kim-Prieto C, Diener E. 2003. Experience sampling: promises and pitfalls, strengths and weaknesses. J. Happiness Stud. 4:15–34
    [Google Scholar]
  119. Seow TXF, Gillan CM. 2020. Transdiagnostic phenotyping reveals a host of metacognitive deficits implicated in compulsivity. Sci. Rep. 10:2883
    [Google Scholar]
  120. Seow TXF, O'Connell R, Gillan CM 2020. Model-based learning deficits in compulsivity are linked to faulty representations of task structure. bioRxiv 2020.06.11.147447. https://doi.org/10.1101/2020.06.11.147447
    [Crossref]
  121. Shapiro DN, Chandler J, Mueller PA. 2013. Using Mechanical Turk to study clinical populations. Clin. Psychol. Sci. 1:2213–20
    [Google Scholar]
  122. Smallwood J, Fitzgerald A, Miles LK, Phillips LH. 2009. Shifting moods, wandering minds: Negative moods lead the mind to wander. Emotion 9:2271–76
    [Google Scholar]
  123. Stewart N, Chandler J, Paolacci G. 2017. Crowdsourcing samples in cognitive science. Trends Cogn. Sci. 21:10736–48
    [Google Scholar]
  124. Stopczynski A, Stahlhut C, Larsen JE, Petersen MK, Hansen LK. 2014. The smartphone brain scanner: a portable real-time neuroimaging system. PLOS ONE 9:2e86733
    [Google Scholar]
  125. Taquet M, Quoidbach J, de Montjoye Y-A, Desseilles M, Gross JJ 2016. Hedonism and the choice of everyday activities. PNAS 113:9769–73
    [Google Scholar]
  126. Taquet M, Quoidbach J, Gross JJ, Saunders KEA, Goodwin GM. 2020. Mood homeostasis, low mood, and history of depression in 2 large population samples. JAMA Psychiatry 77:9944–51
    [Google Scholar]
  127. Triantafillou S, Saeb S, Lattie EG, Mohr DC, Kording KP. 2019. Relationship between sleep quality and mood: ecological momentary assessment study. JMIR Ment. Health 6:3e12613
    [Google Scholar]
  128. Tsuno N, Besset A, Ritchie K. 2005. Sleep and depression. J. Clin. Psychiatry 66:101254–69
    [Google Scholar]
  129. van de Leemput IA, Wichers M, Cramer AO, Borsboom D, Tuerlinckx F et al. 2014. Critical slowing down as early warning for the onset and termination of depression. PNAS 111:187–92
    [Google Scholar]
  130. Villano WJ, Otto AR, Ezie CEC, Gillis R, Heller AS. 2020. Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome. J. Exp. Psychol. Gen. 149:91755–66
    [Google Scholar]
  131. Voon V, Derbyshire K, Rück C, Irvine MA, Worbe Y et al. 2014. Disorders of compulsivity: a common bias towards learning habits. Mol. Psychiatry 20:345–52
    [Google Scholar]
  132. Wang R, Chen F, Chen Z, Li T, Harari G et al. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing3–14 New York: Assoc. Comput. Mach.
    [Google Scholar]
  133. Weitzman ER. 2004. Poor mental health, depression, and associations with alcohol consumption, harm, and abuse in a national sample of young adults in college. J. Nerv. Ment. Dis. 192:4269–77
    [Google Scholar]
  134. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E et al. 2014. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512:7513185–89
    [Google Scholar]
  135. Wichers M, Groot PC, Psychosystems, ESM Group EWS Group 2016. Critical slowing down as a personalized early warning signal for depression. Psychother. Psychosom. 85:2114–16
    [Google Scholar]
  136. Widge AS, Bilge MT, Montana R, Chang W, Rodriguez CI et al. 2019. Electroencephalographic biomarkers for treatment response prediction in major depressive illness: a meta-analysis. Am. J. Psychiatry 176:144–56
    [Google Scholar]
  137. Zung WW. 1965. A self-rating depression scale. Arch. Gen. Psychiatry 12:63–70
    [Google Scholar]
/content/journals/10.1146/annurev-neuro-101220-014053
Loading
/content/journals/10.1146/annurev-neuro-101220-014053
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error