Skip to main content

Advertisement

Springer Nature Link
Log in

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan

  • S.I. : ‘Babel Fish’ for Feature-driven Machine Learning to Maximise Societal Value
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Code availability

For review purposes, the code used is available on application to Dr Andrew Kissane.

References

  1. World Health Organisation (2019) ICD-10 Version:2019. In: World Health Organisation. https://icd.who.int/browse10/2019/en. Accessed 15 Apr 2021

  2. Carvalho AF, Miskowiak KK, Hyphantis TN et al (2014) Cognitive dysfunction in depression–pathophysiology and novel targets. CNS & Neurol Disord-Drug Targets (Former Curr Drug Targets-CNS & Neurol Disord) 13:1819–1835

    Article  Google Scholar 

  3. Parker G, Tavella G, Hadzi-Pavlovic D (2019) Identifying and differentiating melancholic depression in a non-clinical sample. J Affect Disord 243:194–200. https://doi.org/10.1016/j.jad.2018.09.024

    Article  Google Scholar 

  4. Parker G, Bassett D, Outhred T et al (2017) Defining melancholia: a core mood disorder. Bipolar Disord 19:235–237. https://doi.org/10.1111/bdi.12501

    Article  Google Scholar 

  5. Parker G (2017) Diagnosing melancholic depression: some personal observations. Australas Psychiatry 25:21–24. https://doi.org/10.1177/1039856216657696

    Article  Google Scholar 

  6. Ferrari AJ, Charlson FJ, Norman RE et al (2013) Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med 10:e1001547. https://doi.org/10.1371/journal.pmed.1001547

    Article  Google Scholar 

  7. Liu Q, He H, Yang J et al (2020) Changes in the global burden of depression from 1990 to 2017: findings from the Global Burden of Disease study. J Psychiatr Res 126:134–140. https://doi.org/10.1016/j.jpsychires.2019.08.002

    Article  Google Scholar 

  8. Australian Institute of Health and Welfare (2021) Mental Health Services in Australia. AIHW, Australian Government

  9. Mohebbi M, Agustini B, Woods RL et al (2019) Prevalence of depressive symptoms and its associated factors among healthy community-dwelling older adults living in Australia and the United States. Int J Geriatr Psychiatry 34:1208–1216. https://doi.org/10.1002/gps.5119

    Article  Google Scholar 

  10. Goldney RD, Eckert KA, Hawthorne G, Taylor AW (2010) Changes in the prevalence of major depression in an Australian community sample between 1998 and 2008. Aust N Z J Psychiatry 44:901–910. https://doi.org/10.3109/00048674.2010.490520

    Article  Google Scholar 

  11. Brodaty H, Draper B, Saab D et al (2001) Psychosis, depression and behavioural disturbances in Sydney nursing home residents: prevalence and predictors. Int J Geriatr Psychiatry 16:504–512. https://doi.org/10.1002/gps.382

    Article  Google Scholar 

  12. Pirkis J, Pfaff J, Williamson M et al (2009) The community prevalence of depression in older Australians. J Affect Disord 115:54–61. https://doi.org/10.1016/j.jad.2008.08.014

    Article  Google Scholar 

  13. Doran CM, Kinchin I (2019) A review of the economic impact of mental illness. Aust Health Rev 43:43–48. https://doi.org/10.1071/AH16115

    Article  Google Scholar 

  14. Smetanin P, Stiff D, Briante C et al (2011) The Life and Economic Impact of Major Mental Illnesses in Canada. Mental Health Commission of Canada, Ottawa, Canada

  15. McCrone P, Dhanasiri S, Patel A et al (2008) Paying the price—the cost of mental health care in England to 2026. London, England

  16. Hudson S, Trowland H, Russell L (2019) 2018 New Zealand Mental Health Monitor. Wellington, New Zealand

  17. Gasteiger N, Vedhara K, Massey A et al (2021) Depression, anxiety and stress during the COVID-19 pandemic: results from a New Zealand cohort study on mental well-being. BMJ Open 11:e045325. https://doi.org/10.1136/bmjopen-2020-045325

    Article  Google Scholar 

  18. MidCentral District Health Board (2005) Depression Service Plan. MidCentral District Health Board

  19. Murray CJL, Lopez AD (1997) Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 349:1498–1504. https://doi.org/10.1016/S0140-6736(96)07492-2

    Article  Google Scholar 

  20. Nishi D, Ishikawa H, Kawakami N (2019) Prevalence of mental disorders and mental health service use in Japan. Psychiatry Clin Neurosci 73:458–465. https://doi.org/10.1111/pcn.12894

    Article  Google Scholar 

  21. Ishikawa H, Tachimori H, Takeshima T et al (2018) Prevalence, treatment, and the correlates of common mental disorders in the mid 2010’s in Japan: the results of the world mental health Japan 2nd survey. J Affect Disord 241:554–562. https://doi.org/10.1016/j.jad.2018.08.050

    Article  Google Scholar 

  22. Sado M, Yamauchi K, Kawakami N et al (2011) Cost of depression among adults in Japan in 2005. Psychiatry Clin Neurosci 65:442–450. https://doi.org/10.1111/j.1440-1819.2011.02237.x

    Article  Google Scholar 

  23. Okumura Y, Higuchi T (2011) Cost of depression among adults in Japan. Prim Care Companion CNS Disord. https://doi.org/10.4088/PCC.10m01082

    Article  Google Scholar 

  24. Mokkink LB, Terwee CB, Patrick DL et al (2010) The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res 19:539–549. https://doi.org/10.1007/s11136-010-9606-8

    Article  Google Scholar 

  25. El-Den S, Chen TF, Gan Y-L et al (2018) The psychometric properties of depression screening tools in primary healthcare settings: a systematic review. J Affect Disord 225:503–522. https://doi.org/10.1016/j.jad.2017.08.060

    Article  Google Scholar 

  26. Rush AJ, Fava M, Wisniewski SR et al (2004) Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Control Clin Trials 25:119–142. https://doi.org/10.1016/s0197-2456(03)00112-0

    Article  Google Scholar 

  27. Rush AJ, Trivedi MH, Wisniewski SR et al (2006) Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 163:1905–1917. https://doi.org/10.1176/ajp.2006.163.11.1905

    Article  Google Scholar 

  28. Takao Y, Figueroa E, Berna KFJ et al (2021) Validation of a novel online depression symptom severity rating scale: the R8 Depression. Health Qual Life Outcomes 19:163. https://doi.org/10.1186/s12955-020-01654-z

    Article  Google Scholar 

  29. Taniguchi M, Takao Y, Kissane A, Tranter R (2019) Measuring Early Treatment Response and Accelerating Treatment Optimization in the Treatment of Depression: Naturalistic Outcomes from OptiMA1 In: EPA European Congress of Psychiatry 2020

  30. Graham S, Depp C, Lee EE et al (2019) Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep 21:116. https://doi.org/10.1007/s11920-019-1094-0

    Article  Google Scholar 

  31. Su C, Xu Z, Pathak J, Wang F (2020) Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 10:1–26. https://doi.org/10.1038/s41398-020-0780-3

    Article  Google Scholar 

  32. Insel T, Cuthbert B, Garvey M et al (2010) Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 167:748–751. https://doi.org/10.1176/appi.ajp.2010.09091379

    Article  Google Scholar 

  33. Nelson B, McGorry PD, Wichers M et al (2017) Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiat 74:528–534. https://doi.org/10.1001/jamapsychiatry.2017.0001

    Article  Google Scholar 

  34. Tung C, Lu W (2016) Analyzing depression tendency of web posts using an event-driven depression tendency warning model. Artif Intell Med 66:53–62. https://doi.org/10.1016/j.artmed.2015.10.003

    Article  Google Scholar 

  35. Aldarwish MM, Ahmad HF (2017) Predicting depression levels using social media posts. In: 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS). pp. 277–280

  36. Nemesure MD, Heinz MV, Huang R, Jacobson NC (2021) Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci Rep 11:1980. https://doi.org/10.1038/s41598-021-81368-4

    Article  Google Scholar 

  37. Dinga R, Marquand AF, Veltman DJ et al (2018) Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Transl Psychiatry 8:241. https://doi.org/10.1038/s41398-018-0289-1

    Article  Google Scholar 

  38. Wahle F, Kowatsch T, Fleisch E et al (2016) Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR Mhealth Uhealth 4:e111. https://doi.org/10.2196/mhealth.5960

    Article  Google Scholar 

  39. Chekroud AM, Zotti RJ, Shehzad Z et al (2016) Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3:243–250. https://doi.org/10.1016/S2215-0366(15)00471-X

    Article  Google Scholar 

  40. Erguzel TT, Sayar GH, Tarhan N (2016) Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput Appl 27:1607–1616. https://doi.org/10.1007/s00521-015-1959-z

    Article  Google Scholar 

  41. Chattopadhyay S (2017) A neuro-fuzzy approach for the diagnosis of depression. Appl Comput Inform 13:10–18. https://doi.org/10.1016/j.aci.2014.01.001

    Article  Google Scholar 

  42. Vai B, Parenti L, Bollettini I et al (2020) Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging. Eur Neuropsychopharmacol 34:28–38. https://doi.org/10.1016/j.euroneuro.2020.03.008

    Article  Google Scholar 

  43. Richter T, Fishbain B, Markus A et al (2020) Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Sci Rep 10:16381. https://doi.org/10.1038/s41598-020-72289-9

    Article  Google Scholar 

  44. Sharma A, Verbeke WJMI (2020) Improving diagnosis of depression with XGBOOST machine learning model and a large biomarkers Dutch dataset (n = 11,081). Front Big Data 3:15. https://doi.org/10.3389/fdata.2020.00015

    Article  Google Scholar 

  45. Gao S, Calhoun VD, Sui J (2018) Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci Ther 24:1037–1052. https://doi.org/10.1111/cns.13048

    Article  Google Scholar 

  46. Patel MJ, Andreescu C, Price JC et al (2015) Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 30:1056–1067. https://doi.org/10.1002/gps.4262

    Article  Google Scholar 

  47. Jing B, Long Z, Liu H et al (2017) Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases. Oncotarget 8:90452–90464. https://doi.org/10.18632/oncotarget.19860

    Article  Google Scholar 

  48. Yoshida K, Shimizu Y, Yoshimoto J et al (2017) Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLoS ONE 12:e0179638. https://doi.org/10.1371/journal.pone.0179638

    Article  Google Scholar 

  49. Zhong X, Shi H, Ming Q et al (2017) Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples. J Affect Disord 218:346–352. https://doi.org/10.1016/j.jad.2017.04.040

    Article  Google Scholar 

  50. Wang X, Ren Y, Zhang W (2017) Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features. Comput Math Methods Med 2017:3609821. https://doi.org/10.1155/2017/3609821

    Article  MATH  Google Scholar 

  51. Schnyer DM, Clasen PC, Gonzalez C, Beevers CG (2017) Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging 264:1–9. https://doi.org/10.1016/j.pscychresns.2017.03.003

    Article  Google Scholar 

  52. Sundermann B, Feder S, Wersching H et al (2017) Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample. J Neural Transm 124:589–605. https://doi.org/10.1007/s00702-016-1673-8

    Article  Google Scholar 

  53. Bhaumik R, Jenkins LM, Gowins JR et al (2017) Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. Neuroimage Clin 16:390–398. https://doi.org/10.1016/j.nicl.2016.02.018

    Article  Google Scholar 

  54. Ramasubbu R, Brown MRG, Cortese F et al (2016) Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin 12:320–331. https://doi.org/10.1016/j.nicl.2016.07.012

    Article  Google Scholar 

  55. Sacchet MD, Prasad G, Foland-Ross LC et al (2015) Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry 6:21. https://doi.org/10.3389/fpsyt.2015.00021

    Article  Google Scholar 

  56. Sato JR, Moll J, Green S et al (2015) Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res 233:289–291. https://doi.org/10.1016/j.pscychresns.2015.07.001

    Article  Google Scholar 

  57. Johnston BA, Steele JD, Tolomeo S et al (2015) Structural MRI-Based predictions in patients with treatment-refractory depression (TRD). PLoS ONE 10:e0132958. https://doi.org/10.1371/journal.pone.0132958

    Article  Google Scholar 

  58. Shimizu Y, Yoshimoto J, Toki S et al (2015) Toward probabilistic diagnosis and understanding of depression based on functional MRI data analysis with logistic group LASSO. PLoS ONE 10:e0123524. https://doi.org/10.1371/journal.pone.0123524

    Article  Google Scholar 

  59. Rosa MJ, Portugal L, Hahn T et al (2015) Sparse network-based models for patient classification using fMRI. Neuroimage 105:493–506. https://doi.org/10.1016/j.neuroimage.2014.11.021

    Article  Google Scholar 

  60. Cao L, Guo S, Xue Z et al (2014) Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci 68:110–119. https://doi.org/10.1111/pcn.12106

    Article  Google Scholar 

  61. Zeng L-L, Shen H, Liu L, Hu D (2014) Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp 35:1630–1641. https://doi.org/10.1002/hbm.22278

    Article  Google Scholar 

  62. Habes I, Krall SC, Johnston SJ et al (2013) Pattern classification of valence in depression. Neuroimage Clin 2:675–683. https://doi.org/10.1016/j.nicl.2013.05.001

    Article  Google Scholar 

  63. Wei M, Qin J, Yan R et al (2013) Identifying major depressive disorder using Hurst exponent of resting-state brain networks. Psychiatry Res 214:306–312. https://doi.org/10.1016/j.pscychresns.2013.09.008

    Article  Google Scholar 

  64. Mwangi B, Ebmeier KP, Matthews K, Steele JD (2012) Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 135:1508–1521. https://doi.org/10.1093/brain/aws084

    Article  Google Scholar 

  65. Mourão-Miranda J, Hardoon DR, Hahn T et al (2011) Patient classification as an outlier detection problem: an application of the one-class support vector machine. Neuroimage 58:793–804. https://doi.org/10.1016/j.neuroimage.2011.06.042

    Article  Google Scholar 

  66. Schmaal L, Marquand AF, Rhebergen D et al (2015) Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biol Psychiatry 78:278–286. https://doi.org/10.1016/j.biopsych.2014.11.018

    Article  Google Scholar 

  67. Frangou S, Dima D, Jogia J (2017) Towards person-centered neuroimaging markers for resilience and vulnerability in bipolar disorder. Neuroimage 145:230–237. https://doi.org/10.1016/j.neuroimage.2016.08.066

    Article  Google Scholar 

  68. Gao S, Osuch EA, Wammes M et al (2017) Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components. In: 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). ieeexplore.ieee.org, pp. 1–6

  69. Jie N-F, Zhu M-H, Ma X-Y et al (2015) Discriminating bipolar disorder from major depression based on SVM-FoBa: efficient feature selection with multimodal brain imaging data. IEEE Trans Auton Ment Dev 7:320–331. https://doi.org/10.1109/TAMD.2015.2440298

    Article  Google Scholar 

  70. Rubin-Falcone H, Zanderigo F, Thapa-Chhetry B et al (2018) Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder. J Affect Disord 227:498–505. https://doi.org/10.1016/j.jad.2017.11.043

    Article  Google Scholar 

  71. Deng F, Wang Y, Huang H et al (2018) Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression. Prog Neuropsychopharmacol Biol Psychiatry 81:340–349. https://doi.org/10.1016/j.pnpbp.2017.09.006

    Article  Google Scholar 

  72. Li M, Das T, Deng W et al (2017) Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand 136:288–299. https://doi.org/10.1111/acps.12752

    Article  Google Scholar 

  73. He H, Sui J, Du Y et al (2017) Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders. Brain Struct Funct 222:4051–4064. https://doi.org/10.1007/s00429-017-1451-x

    Article  Google Scholar 

  74. Bürger C, Redlich R, Grotegerd D et al (2017) Differential abnormal pattern of anterior cingulate gyrus activation in unipolar and bipolar depression: an fMRI and pattern classification approach. Neuropsychopharmacology 42:1399–1408. https://doi.org/10.1038/npp.2017.36

    Article  Google Scholar 

  75. Rive MM, Redlich R, Schmaal L, Marquand AF (2016) Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters. Bipolar Disord. https://doi.org/10.1111/bdi.12446

    Article  Google Scholar 

  76. Sacchet MD, Livermore EE, Iglesias JE et al (2015) Subcortical volumes differentiate major depressive disorder, bipolar disorder, and remitted major depressive disorder. J Psychiatr Res 68:91–98. https://doi.org/10.1016/j.jpsychires.2015.06.002

    Article  Google Scholar 

  77. Fung G, Deng Y, Zhao Q et al (2015) Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study. BMC Psychiatry 15:298. https://doi.org/10.1186/s12888-015-0685-5

    Article  Google Scholar 

  78. Redlich R, Almeida JR, Grotegerd D et al (2014) Brain morphometric biomarkers distinguishing unipolar and bipolar depression: a voxel-based morphometry-pattern classification approach. JAMA Psychiatry 71:1222–1230. https://doi.org/10.1001/jamapsychiatry.2014.1100

    Article  Google Scholar 

  79. MacMaster FP, Carrey N, Langevin LM et al (2014) Disorder-specific volumetric brain difference in adolescent major depressive disorder and bipolar depression. Brain Imaging Behav 8:119–127. https://doi.org/10.1007/s11682-013-9264-x

    Article  Google Scholar 

  80. Serpa MH, Ou Y, Schaufelberger MS et al (2014) Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability. Biomed Res Int 2014:706157. https://doi.org/10.1155/2014/706157

    Article  Google Scholar 

  81. Grotegerd D, Stuhrmann A, Kugel H (2014) Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression: an fMRI and pattern classification study. Hum Brain Mapp. https://doi.org/10.1002/hbm.22380

    Article  Google Scholar 

  82. Zeng L-L, Shen H, Liu L et al (2012) Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135:1498–1507. https://doi.org/10.1093/brain/aws059

    Article  Google Scholar 

  83. Hilbert K, Lueken U, Muehlhan M, Beesdo-Baum K (2017) Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: a multimodal machine learning study. Brain Behav 7:e00633. https://doi.org/10.1002/brb3.633

    Article  Google Scholar 

  84. Koutsouleris N, Meisenzahl EM, Borgwardt S et al (2015) Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 138:2059–2073. https://doi.org/10.1093/brain/awv111

    Article  Google Scholar 

  85. Yu Y, Shen H, Zeng L-L et al (2013) Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLoS ONE 8:e68250. https://doi.org/10.1371/journal.pone.0068250

    Article  Google Scholar 

  86. Yang W, Chen Q, Liu P et al (2016) Abnormal brain activation during directed forgetting of negative memory in depressed patients. J Affect Disord 190:880–888. https://doi.org/10.1016/j.jad.2015.05.034

    Article  Google Scholar 

  87. Johnston BA, Tolomeo S, Gradin V, Christmas D (2015) Failure of hippocampal deactivation during loss events in treatment-resistant depression. Brain. https://doi.org/10.1093/brain/awv177

    Article  Google Scholar 

  88. Guo H, Cheng C, Cao X et al (2014) Resting-state functional connectivity abnormalities in first-onset unmedicated depression. Neural Regen Res 9:153–163. https://doi.org/10.4103/1673-5374.125344

    Article  Google Scholar 

  89. Modinos G, Mechelli A, Pettersson-Yeo W et al (2013) Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor. PeerJ 1:e42. https://doi.org/10.7717/peerj.42

    Article  Google Scholar 

  90. Ma Z, Li R, Yu J et al (2013) Alterations in regional homogeneity of spontaneous brain activity in late-life subthreshold depression. PLoS ONE 8:e53148. https://doi.org/10.1371/journal.pone.0053148

    Article  Google Scholar 

  91. Grotegerd D, Suslow T, Bauer J et al (2013) Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study. Eur Arch Psychiatry Clin Neurosci 263:119–131. https://doi.org/10.1007/s00406-012-0329-4

    Article  Google Scholar 

  92. Fang P, Zeng L-L, Shen H et al (2012) Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging. PLoS ONE 7:e45972. https://doi.org/10.1371/journal.pone.0045972

    Article  Google Scholar 

  93. Lord A, Horn D, Breakspear M, Walter M (2012) Changes in community structure of resting state functional connectivity in unipolar depression. PLoS ONE 7:e41282. https://doi.org/10.1371/journal.pone.0041282

    Article  Google Scholar 

  94. Lythe KE, Moll J, Gethin JA et al (2015) Self-blame–selective hyperconnectivity between anterior temporal and subgenual cortices and prediction of recurrent depressive episodes. JAMA Psychiatry 72:1119–1126. https://doi.org/10.1001/jamapsychiatry.2015.1813

    Article  Google Scholar 

  95. Liu F, Guo W, Yu D et al (2012) Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS ONE 7:e40968. https://doi.org/10.1371/journal.pone.0040968

    Article  Google Scholar 

  96. Korgaonkar MS, Rekshan W, Gordon E et al (2015) Magnetic resonance imaging measures of brain structure to predict antidepressant treatment outcome in major depressive disorder. EBioMedicine 2:37–45. https://doi.org/10.1016/j.ebiom.2014.12.002

    Article  Google Scholar 

  97. Williams LM, Korgaonkar MS, Song YC et al (2015) Amygdala reactivity to emotional faces in the prediction of general and medication-specific responses to antidepressant treatment in the randomized iSPOT-D trial. Neuropsychopharmacology 40:2398–2408. https://doi.org/10.1038/npp.2015.89

    Article  Google Scholar 

  98. Korgaonkar MS, Williams LM, Song YJ et al (2014) Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder. Br J Psychiatry 205:321–328. https://doi.org/10.1192/bjp.bp.113.140376

    Article  Google Scholar 

  99. Gong Q, Wu Q, Scarpazza C et al (2011) Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage 55:1497–1503. https://doi.org/10.1016/j.neuroimage.2010.11.079

    Article  Google Scholar 

  100. Costafreda SG, Khanna A, Mourao-Miranda J, Fu CHY (2009) Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. NeuroReport 20:637–641. https://doi.org/10.1097/WNR.0b013e3283294159

    Article  Google Scholar 

  101. Drysdale AT, Grosenick L, Downar J et al (2017) Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 23:28–38. https://doi.org/10.1038/nm.4246

    Article  Google Scholar 

  102. Sankar A, Zhang T, Gaonkar B et al (2016) Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample. BJPsych Open 2:247–254. https://doi.org/10.1192/bjpo.bp.115.002493

    Article  Google Scholar 

  103. Foland-Ross LC, Sacchet MD, Prasad G et al (2015) Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci 46:125–131. https://doi.org/10.1016/j.ijdevneu.2015.07.007

    Article  Google Scholar 

  104. Rondina JM, Hahn T, de Oliveira L et al (2014) SCoRS—A Method Based on Stability for Feature Selection and Mapping in Neuroimaging. IEEE Trans Med Imaging 33:85–98. https://doi.org/10.1109/TMI.2013.2281398

    Article  Google Scholar 

  105. Hahn T, Marquand AF, Ehlis A-C et al (2011) Integrating neurobiological markers of depression. Arch Gen Psychiatry 68:361–368. https://doi.org/10.1001/archgenpsychiatry.2010.178

    Article  Google Scholar 

  106. Nouretdinov I, Costafreda SG, Gammerman A et al (2011) Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage 56:809–813. https://doi.org/10.1016/j.neuroimage.2010.05.023

    Article  Google Scholar 

  107. Costafreda SG, Chu C, Ashburner J, Fu CHY (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4:e6353. https://doi.org/10.1371/journal.pone.0006353

    Article  Google Scholar 

  108. Fu CHY, Mourao-Miranda J, Costafreda SG et al (2008) Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 63:656–662. https://doi.org/10.1016/j.biopsych.2007.08.020

    Article  Google Scholar 

  109. Van Waarde JA, Scholte HS, Van Oudheusden LJB et al (2015) A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 20:609–614

    Article  Google Scholar 

  110. Jiang R, Abbott CC, Jiang T et al (2018) SMRI biomarkers predict electroconvulsive treatment outcomes: accuracy with independent data sets. Neuropsychopharmacology 43:1078–1087. https://doi.org/10.1038/npp.2017.165

    Article  Google Scholar 

  111. Redlich R, Opel N, Grotegerd D et al (2016) Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry 73:557–564. https://doi.org/10.1001/jamapsychiatry.2016.0316

    Article  Google Scholar 

  112. de Nijs J, Burger TJ, Janssen RJ et al (2021) Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach. Npj Schizophr 7:1–11. https://doi.org/10.1038/s41537-021-00162-3

    Article  Google Scholar 

  113. Taliaz D, Spinrad A, Barzilay R et al (2021) Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry 11:1–9. https://doi.org/10.1038/s41398-021-01488-3

    Article  Google Scholar 

  114. Smith DJ, Griffiths E, Kelly M et al (2011) Unrecognised bipolar disorder in primary care patients with depression. Br J Psychiatry 199:49–56. https://doi.org/10.1192/bjp.bp.110.083840

    Article  Google Scholar 

  115. Spitzer RL, Kroenke K, Williams JBW, Löwe B (2006) A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 166:1092–1097. https://doi.org/10.1001/archinte.166.10.1092

    Article  Google Scholar 

  116. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is All you Need. In: Guyon I, Luxburg UV, Bengio S, et al (eds) Advances in Neural Information Processing Systems. Curran Associates, Inc

  117. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  118. Kabaila R (2021) Implementation of an innovative nurse led service to support treatment for depression in primary care (OptiMA2). Aust Nurs Midwifery J 27:22–23

    Google Scholar 

  119. Higuchi T (2010) Major depressive disorder treatment guidelines in Japan. J Clin Psychiatry 71(Suppl E1):e05. https://doi.org/10.4088/JCP.9058se1c.05gry

    Article  Google Scholar 

  120. Malhi GS, Bell E, Bassett D et al (2021) The 2020 Royal Australian and New Zealand college of psychiatrists clinical practice guidelines for mood disorders. Aust N Z J Psychiatry 55:7–117. https://doi.org/10.1177/0004867420979353

    Article  Google Scholar 

  121. van den Oord A, Dieleman S, Zen H et al (2016) WaveNet: a generative model for raw audio. arXiv [cs.SD]

  122. Krog MD, Nielsen MG, Le JV et al (2018) Barriers and facilitators to using a web-based tool for diagnosis and monitoring of patients with depression: a qualitative study among Danish general practitioners. BMC Health Serv Res 18:503. https://doi.org/10.1186/s12913-018-3309-1

    Article  Google Scholar 

  123. Cuijpers P, Quero S, Dowrick C, Arroll B (2019) Psychological treatment of depression in primary care: recent developments. Curr Psychiatry Rep 21:129. https://doi.org/10.1007/s11920-019-1117-x

    Article  Google Scholar 

  124. Bedirhan Üstün T, Sartorius N (1995) Mental illness in general health care: an international study. Geneva, Switzerland

  125. Bijl RV, Ravelli A (2000) Psychiatric morbidity, service use, and need for care in the general population: results of The Netherlands mental health survey and incidence study. Am J Public Health 90:602–607. https://doi.org/10.2105/ajph.90.4.602

    Article  Google Scholar 

  126. Bortolotti B, Menchetti M, Bellini F et al (2008) Psychological interventions for major depression in primary care: a meta-analytic review of randomized controlled trials. Gen Hosp Psychiatry 30:293–302. https://doi.org/10.1016/j.genhosppsych.2008.04.001

    Article  Google Scholar 

  127. García-Lizana F, Muñoz-Mayorga I (2010) Telemedicine for depression: a systematic review. Perspect Psychiatr Care 46:119–126. https://doi.org/10.1111/j.1744-6163.2010.00247.x

    Article  Google Scholar 

  128. Wade AG (2010) Use of the internet to assist in the treatment of depression and anxiety: a systematic review. Prim Care Companion J Clin Psychiatry. https://doi.org/10.4088/PCC.09r00876blu

    Article  Google Scholar 

  129. Berger M, Wagner TH, Baker LC (2005) Internet use and stigmatized illness. Soc Sci Med 61:1821–1827. https://doi.org/10.1016/j.socscimed.2005.03.025

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Andrew Kissane and Dr. Richard Tranter for allowing the use of the Psynary data, their ongoing support and professional input into the research work. Aidan Cousins is a 4th year medical student with University of New South Wales undertaking his Honours research project.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, private or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Faculty of Medicine and Health, Rural Clinical School, University of New South Wales, Port Macquarie, 2444, Australia

    Aidan Cousins & Emma Schofield

  2. Sofia University, Tokyo, Japan

    Lucas Nakano

  3. Mid North Coast Local Health District, Community Mental Health Services, Port Macquarie, 2444, Australia

    Rasa Kabaila

Authors
  1. Aidan Cousins
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lucas Nakano
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Emma Schofield
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Rasa Kabaila
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Aidan Cousins and Lucas Nakano were involved in study design, implementation and analysis and drafting of the manuscript. Rasa Kabaila and Emma Schofield were involved in study design and expert review.

Corresponding author

Correspondence to Aidan Cousins.

Ethics declarations

Conflict of interests

The authors declare that they have no conflicts of interest.

Ethics approval

This research was approved by the clinical research ethics committee of University of Otago (New Zealand, approval #:H16/014), Asai Hifuka Institutional Review Board (Japan, approval #:承認番号: 20170724-1) and the North Coast NSW Human Research Ethics Committee (Australia, approval #:HREA271 2019/ETH13489).

Consent to participate

Written consent was obtained from all participants for use of de-identified data from Psynary for the purpose of analysing the naturalistic clinical outcomes.

Consent for publication

All authors approve the final version of the manuscript and this submission for possible publication in Neural Computing and Applications.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cousins, A., Nakano, L., Schofield, E. et al. A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput & Applic 35, 11497–11516 (2023). https://doi.org/10.1007/s00521-021-06710-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06710-3

Keywords