Top

BMC Psychiatry

Published in:

Open Access 01-12-2021 | Research

Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

Authors: Hanxiaoran Li, Sutao Song, Donglin Wang, Zhonglin Tan, Zhenzhen Lian, Yan Wang, Xin Zhou, Chenyuan Pan

Published in: BMC Psychiatry | Issue 1/2021

Abstract

Background

Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA).

Methods

Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus.

Results

The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r² = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r² = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification.

Conclusions

The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

Available only for authorised users

Dunner DL. Major depressive disorder. Med Basis Psychiatry Third Ed. 2008:73–83. https://doi.org/10.1007/978-1-59745-252-6_5.

Bilello JA. Seeking an objective diagnosis of depression. Biomark Med. 2016;10(8):861–75. https://doi.org/10.2217/bmm-2016-0076.CrossRefPubMed

Busatto GF. Structural and functional neuroimaging studies in major depressive disorder with psychotic features: a critical review. Schizophr Bull. 2013;39(4):776–86. https://doi.org/10.1093/schbul/sbt054.CrossRefPubMedPubMedCentral

Graham J, Salimi-Khorshidi G, Hagan C, Walsh N, Goodyer I, Lennox B, et al. Meta-analytic evidence for neuroimaging models of depression: state or trait? J Affect Disord. 2013;151(2):423–31. https://doi.org/10.1016/j.jad.2013.07.002.CrossRefPubMed

Depeursinge A, Racoceanu D, Iavindrasana J, et al. Fusing Visual and Clinical Information for Lung Tissue Classification in HRCT Data. Artif Intell Med. 2010;229:ARTMED1118. https://doi.org/10.1016/j.artmed.2010.04.006.CrossRef

Xue SW, Wang D, Tan Z, Wang Y, Lian Z, Sun Y, et al. Disrupted brain entropy and functional connectivity patterns of thalamic subregions in major depressive disorder. Neuropsychiatr Dis Treat. 2019;15:2629–38. https://doi.org/10.2147/NDT.S220743.CrossRefPubMedPubMedCentral

Hahn T, Marquand AF, Ehlis AC, Dresler T, Kittel-Schneider S, Jarczok TA, et al. Integrating neurobiological markers of depression. Arch Gen Psychiatry. 2011;68(4):361–8. https://doi.org/10.1001/archgenpsychiatry.2010.178.CrossRefPubMed

Lopez-Larson M, King JB, McGlade E, et al. Enlarged thalamic volumes and increased fractional anisotropy in the thalamic radiations in veterans with suicide behaviors. Front Psychiatry. 2013;4(AUG):1–13. https://doi.org/10.3389/fpsyt.2013.00083.CrossRef

Yamamura T, Okamoto Y, Okada G, et al. Association of thalamic hyperactivity with treatment-resistant depression and poorresponse in early treatment for major depression: A resting-state fMRI study using fractional amplitude of low-frequencyfluctuations. Transl Psychiatry. 2016;6(3). https://doi.org/10.1038/tp.2016.18.

10.

Yuan R, Di X, Taylor PA, Gohel S, Tsai YH, Biswal BB. Functional topography of the thalamocortical system in human. Brain Struct Funct. 2016;221(4):1971–84. https://doi.org/10.1007/s00429-015-1018-7.CrossRefPubMed

11.

Taber KH, Wen C, Khan A, Hurley RA. The limbic thalamus. J Neuropsychiatry Clin Neurosci. 2004;16(2):127–32. https://doi.org/10.1176/appi.neuropsych.16.2.127.CrossRefPubMed

12.

Sherman SM. Thalamus plays a central role in ongoing cortical functioning. Nat Neurosci. 2016;19(4):533–41. https://doi.org/10.1038/nn.4269.CrossRefPubMed

13.

Hwang K, Bertolero MA, Liu WB, D’Esposito M. The human thalamus is an integrative hub for functional brain networks. J Neurosci. 2017;37(23):5594–607. https://doi.org/10.1523/JNEUROSCI.0067-17.2017.CrossRefPubMedPubMedCentral

14.

Peters SK, Dunlop K, Downar J. Cortico-striatal-thalamic loop circuits of the salience network: A central pathway in psychiatric disease and treatment. Front Syst Neurosci. 2016;10(DEC):1–23. https://doi.org/10.3389/fnsys.2016.00104.CrossRef

15.

Deng F, Wang Y, Huang H, Niu M, Zhong S, Zhao L, et al. Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression. Prog Neuro-Psychopharmacology Biol Psychiatry. 2018;81:340–9. https://doi.org/10.1016/j.pnpbp.2017.09.006.CrossRef

16.

Zhao YJ, Du MY, Huang XQ, et al. Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis. Psychol Med. 2014;44(14):2927–37. https://doi.org/10.1017/S0033291714000518.CrossRefPubMed

17.

Jenkins L, Chiang J, Alpert K, Wang L, Miller G. F53. Outward Subcortical Deformations Associated With Sub-Clinical Depression Symptoms in Adolescents. Biol Psychiatry. 2018;83(9):S258. https://doi.org/10.1016/j.biopsych.2018.02.666.CrossRef

18.

Lu Y, Liang H, Han D, Mo Y, Li Z, Cheng Y, et al. The volumetric and shape changes of the putamen and thalamus in first episode, untreated major depressive disorder. NeuroImage Clin. 2016;11:658–66. https://doi.org/10.1016/j.nicl.2016.04.008.CrossRefPubMedPubMedCentral

19.

Zhang X, Yao S, Zhu X, Wang X, Zhu X, Zhong M. Gray matter volume abnormalities in individuals with cognitive vulnerability to depression: a voxel-based morphometry study. J Affect Disord. 2012;136(3):443–52. https://doi.org/10.1016/j.jad.2011.11.005.CrossRefPubMed

20.

Nugent AC, Davis RM, Zarate CA, Drevets WC. Reduced thalamic volumes in major depressive disorder. Psychiatry Res Neuroimaging. 2013;213(3):179–85. https://doi.org/10.1016/j.pscychresns.2013.05.004.CrossRef

21.

Ahn SJ, Kyeong S, Suh SH, Kim JJ, Chung TS, Seok JH. What is the impact of child abuse on gray matter abnormalities in individuals with major depressive disorder: a case control study. BMC Psychiatry. 2016;16(1):1–7. https://doi.org/10.1186/s12888-016-1116-y.CrossRef

22.

Hagan CC, Graham JME, Tait R, Widmer B, van Nieuwenhuizen AO, Ooi C, et al. Adolescents with current major depressive disorder show dissimilar patterns of age-related differences in ACC and thalamus. NeuroImage Clin. 2015;7:391–9. https://doi.org/10.1016/j.nicl.2014.12.019.CrossRefPubMedPubMedCentral

23.

Bora E, Harrison BJ, Davey CG, Yücel M, Pantelis C. Meta-analysis of volumetric abnormalities in cortico-striatal-pallidal- thalamic circuits in major depressive disorder. Psychol Med. 2012;42(4):671–81. https://doi.org/10.1017/S0033291711001668.CrossRefPubMed

24.

Webb CA, Weber M, Mundy EA, Killgore WDS. Reduced gray matter volume in the anterior cingulate, orbitofrontal cortex and thalamus as a function of mild depressive symptoms: a voxel-based morphometric analysis. Psychol Med. 2014;44(13):2833–43. https://doi.org/10.1017/S0033291714000348.CrossRefPubMedPubMedCentral

25.

Li J, Wang Z, Hwang JW, Zhao B, Yang X, Xin S, et al. Anatomical brain difference of subthreshold depression in young and middle-aged individuals. NeuroImage Clin. 2017;14:546–51. https://doi.org/10.1016/j.nicl.2017.02.022.CrossRefPubMedPubMedCentral

26.

Takamiya A, Kishimoto T. Liang K ching, et al. thalamic volume, resting-state activity, and their association with the efficacy of electroconvulsive therapy. J Psychiatr Res. 2019;117:135–41. https://doi.org/10.1016/j.jpsychires.2019.08.001.CrossRefPubMed

27.

Buckner RL, DiNicola LM. The brain’s default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci. 2019;20(10):593–608. https://doi.org/10.1038/s41583-019-0212-7.CrossRefPubMed

28.

Pinault D. The Thalamic Reticular Nucleus: Structure, Function and Concept. Brain Res Brain Res Rev. 2004;46(1):1–31. https://doi.org/10.1016/j.brainresrev.2004.04.008.CrossRefPubMed

29.

Haxby JV. Multivariate pattern analysis of fMRI: the early beginnings. Neuroimage. 2012;62(2):852–5. https://doi.org/10.1016/j.neuroimage.2012.03.016.CrossRefPubMed

30.

Foland-Ross LC, Sacchet MD, Prasad G, Gilbert B, Thompson PM, Gotlib IH. Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci. 2015;46(1):125–31. https://doi.org/10.1016/j.ijdevneu.2015.07.007.CrossRefPubMedPubMedCentral

31.

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

32.

Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain. 2012;135(5):1498–507. https://doi.org/10.1093/brain/aws059.CrossRefPubMed

33.

Cao L, Guo S, Xue Z, Hu Y, Liu H, Mwansisya TE, et al. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci. 2014;68(2):110–9. https://doi.org/10.1111/pcn.12106.CrossRefPubMed

34.

Gao S, Calhoun VD, Sui J. Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci Ther. 2018;24(11):1037–52. https://doi.org/10.1111/cns.13048.CrossRefPubMedPubMedCentral

35.

Sankar A, Zhang T, Gaonkar B, Doshi J, Erus G, Costafreda SG, et al. Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample. BJPsych Open. 2016;2(04):247–54. https://doi.org/10.1192/bjpo.bp.115.002493.CrossRefPubMedPubMedCentral

36.

MacMaster FP, Carrey N, Langevin LM, Jaworska N, Crawford S. Disorder-specific volumetric brain difference in adolescent major depressive disorder and bipolar depression. Brain Imaging Behav. 2014;8(1):119–27. https://doi.org/10.1007/s11682-013-9264-x.CrossRefPubMed

37.

Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, et al. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry. 2016;73(6):557–64. https://doi.org/10.1001/jamapsychiatry.2016.0316.CrossRefPubMed

38.

Costafreda SG, Khanna A, Mourao-Miranda J, Fu CHY. Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. Neuroreport. 2009;20(7):637–41. https://doi.org/10.1097/WNR.0b013e3283294159.CrossRefPubMed

39.

Bürger C, Redlich R, Grotegerd D, Meinert S, Dohm K, Schneider I, et al. Differential abnormal pattern of anterior cingulate gyrus activation in unipolar and bipolar depression: an fMRI and pattern classification approach. Neuropsychopharmacology. 2017;42(7):1399–408. https://doi.org/10.1038/npp.2017.36.CrossRefPubMedPubMedCentral

40.

Frangou S, Dima D, Jogia J. Towards person-centered neuroimaging markers for resilience and vulnerability in bipolar disorder. Neuroimage. 2017;145(Pt B):230–7. https://doi.org/10.1016/j.neuroimage.2016.08.066.CrossRefPubMed

41.

Rive MM, Redlich R, Schmaal L, Marquand AF, Dannlowski U, Grotegerd D, et al. Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters. Bipolar Disord. 2016;18(7):612–23. https://doi.org/10.1111/bdi.12446.CrossRefPubMed

42.

Redlich R, Almeida JR, Grotegerd D, et al. Brain morphometric biomarkers distinguishing unipolar and bipolar depression: a voxel-based morphometry-pattern classification approach. JAMA Psychiatry. 2014;71(11):1222–30. https://doi.org/10.1001/jamapsychiatry.2014.1100.CrossRefPubMedPubMedCentral

43.

Grotegerd D, Stuhrmann A, Kugel H, Schmidt S, Redlich R, Zwanzger P, et al. Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression: an fMRI and pattern classification study. Hum Brain Mapp. 2014;35(7):2995–3007. https://doi.org/10.1002/hbm.22380.CrossRefPubMed

44.

Grotegerd D, Suslow T, Bauer J, Ohrmann P, Arolt V, Stuhrmann A, et al. Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study. Eur Arch Psychiatry Clin Neurosci. 2013;263(2):119–31. https://doi.org/10.1007/s00406-012-0329-4.CrossRefPubMed

45.

Schmaal L, Marquand AF, Rhebergen D, van Tol MJ, Ruhé HG, van der Wee NJA, et al. Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biol Psychiatry. 2015;78(4):278–86. https://doi.org/10.1016/j.biopsych.2014.11.018.CrossRefPubMedPubMedCentral

46.

Marquand A, Howard M, Brammer M, Chu C, Coen S, Mourão-Miranda J. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage. 2010;49(3):2178–89. https://doi.org/10.1016/j.neuroimage.2009.10.072.CrossRefPubMed

47.

Schrouff J, Monteiro JM, Portugal L, Rosa MJ, Phillips C, Mourão-Miranda J. Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models. Neuroinformatics. 2018;16(1):117–43. https://doi.org/10.1007/s12021-017-9347-8.CrossRefPubMedPubMedCentral

48.

Johnson-Lawrence V, Scott JB, James SA. Education, perceived discrimination and risk for depression in a southern black cohort. Aging Ment Health 2019;0(0):1–7. doi:https://doi.org/10.1080/13607863.2019.1647131, 24, 11.

49.

Pearson RM, Evans J, Kounali D, Lewis G, Heron J, Ramchandani PG, et al. Maternal depression during pregnancy and the postnatal period risks and possible mechanisms for offspring depression at age 18 years. JAMA Psychiatry. 2013;70(12):1312–9. https://doi.org/10.1001/jamapsychiatry.2013.2163.CrossRefPubMedPubMedCentral

50.

Chang-Quan H, Zheng-Rong W, Yong-Hong L, Yi-Zhou X, Qing-Xiu L. Education and risk for late life depression: a meta-analysis of published literature. Int J Psychiatry Med. 2010;40(1):109–24. https://doi.org/10.2190/PM.40.1.i.CrossRefPubMed

51.

Ladin K. Risk of late-life depression across 10 European Union countries: deconstructing the education effect. J Aging Health. 2008;20(6):653–70. https://doi.org/10.1177/0898264308321002.CrossRefPubMed

52.

Park AL, Fuhrer R, Quesnel-Vallée A. Parents’ education and the risk of major depression in early adulthood. Soc Psychiatry Psychiatr Epidemiol. 2013;48(11):1829–39. https://doi.org/10.1007/s00127-013-0697-8.CrossRefPubMed

53.

Yan CG, Di Wang X, Zuo XN, Zang YF. DPABI: Data Processing & Analysis for (resting-state) brain imaging. Neuroinformatics. 2016;14(3):339–51. https://doi.org/10.1007/s12021-016-9299-4.CrossRefPubMed

54.

Ma X, Liu J, Liu T, Ma L, Wang W, Shi S, et al. Altered resting-state functional activity in medication-naive patients with first-episode major depression disorder vs. healthy control: a quantitative meta-analysis. Front Behav Neurosci. 2019;13(May):1–11. https://doi.org/10.3389/fnbeh.2019.00089.CrossRef

55.

Hao HH, Chen C, Mao WB, Zhong JG, Dai ZY. Aberrant brain regional homogeneity in first-episode drug-naïve patients with major depressive disorder: a voxel-wise meta-analysis. J Affect Disord. 2019;245:63–71. https://doi.org/10.1016/j.jad.2018.10.113.CrossRefPubMed

56.

Song Y, Shen X, Mu X, Mao N, Wang B. A study on BOLD fMRI of the brain basic activities of MDD and the first-degree relatives. Int J Psychiatry Clin Pract. 2020;0(0):1–9. https://doi.org/10.1080/13651501.2020.1744663.CrossRef

57.

Yang Y, Liu S, Jiang X, et al. Common and specific functional activity features in schizophrenia, major depressive disorder, and bipolar disorder. Front Psychiatry. 2019;10(FEB):1–10. https://doi.org/10.3389/fpsyt.2019.00052.CrossRef

58.

Gray JP, Müller VI, Eickhoff SB, Fox PT. Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am J Psychiatry. 2020;177(5):422–34. https://doi.org/10.1176/appi.ajp.2019.19050560.CrossRefPubMedPubMedCentral

59.

Li G, Rossbach K, Zhang A, Liu P, Zhang K. Resting-state functional changes in the precuneus within first-episode drug-naive patients with MDD. Neuropsychiatr Dis Treat. 2018;14:1991–8. https://doi.org/10.2147/NDT.S168060.CrossRefPubMedPubMedCentral

60.

Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008;172(1):137–41. https://doi.org/10.1016/j.jneumeth.2008.04.012.CrossRefPubMedPubMedCentral

61.

Lui S, Huang X, Chen L, Tang H, Zhang T, Li X, et al. High-field MRI reveals an acute impact on brain function in survivors of the magnitude 8.0 earthquake in China. Proc Natl Acad Sci. 2009;106(36):15412–7. https://doi.org/10.1073/pnas.0812751106.CrossRefPubMedPubMedCentral

62.

Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34(4):537–41. https://doi.org/10.1002/mrm.1910340409.CrossRefPubMed

63.

Liu F, Guo W, Liu L, Long Z, Ma C, Xue Z, et al. Abnormal amplitude low-frequency oscillations in medication-naive, first-episode patients with major depressive disorder: a resting-state fMRI study. J Affect Disord. 2013;146(3):401–6. https://doi.org/10.1016/j.jad.2012.10.001.CrossRefPubMed

64.

Schrouff J, Rosa MJ, Rondina JM, Marquand AF, Chu C, Ashburner J, et al. PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics. 2013;11(3):319–37. https://doi.org/10.1007/s12021-013-9178-1.CrossRefPubMedPubMedCentral

65.

Hewing L, Kabzan J, Zeilinger MN. Cautious Model Predictive Control Using Gaussian Process Regression. IEEE Trans Control Syst Technol. 2019;PP:1–8. https://doi.org/10.1109/TCST.2019.2949757.CrossRef

66.

Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al. The human Brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex. 2016;26(8):3508–26. https://doi.org/10.1093/cercor/bhw157.CrossRefPubMedPubMedCentral

67.

Guillery RW, Sherman SM. Thalamic relay functions and their role in corticocortical communication: generalizations from the visual system. Neuron. 2002;33(2):163–75. https://doi.org/10.1016/S0896-6273(01)00582-7.CrossRefPubMed

68.

Shadrina M, Bondarenko EA, Slominsky PA. Genetics factors in major depression disease. Front Psychiatry. 2018;9:73–83.CrossRef

69.

Zang YF, Yong H, Chao-Zhe Z, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007;29(2):83–91. https://doi.org/10.1016/j.braindev.2006.07.002.CrossRefPubMed

70.

Shu Y, Kuang L, Huang Q, He L. Fractional amplitude of low-frequency fluctuation (fALFF) alterations in young depressed patients with suicide attempts after cognitive behavioral therapy and antidepressant medication cotherapy: a resting-state fMRI study. J Affect Disord. 2020;276:822–8. https://doi.org/10.1016/j.jad.2020.07.038.CrossRefPubMed

71.

Patel MJ, Khalaf A, Aizenstein HJ. Studying depression using imaging and machine learning methods. NeuroImage Clin. 2016;10:115–23. https://doi.org/10.1016/j.nicl.2015.11.003.CrossRefPubMed

Title: Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features
Authors: Hanxiaoran Li
Sutao Song
Donglin Wang
Zhonglin Tan
Zhenzhen Lian
Yan Wang
Xin Zhou
Chenyuan Pan
Publication date: 01-12-2021
Publisher: BioMed Central
Published in: BMC Psychiatry / Issue 1/2021
Electronic ISSN: 1471-244X
DOI: https://doi.org/10.1186/s12888-021-03414-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Person-centered, non-pharmacological intervention in reducing psychotropic medications use among residents with dementia in Australian rural aged care homes

Resilience, coping, and distress among healthcare service personnel during the COVID-19 pandemic

Mathematical pattern of Kessler psychological distress distribution in the general population of the U.S. and Japan

Correction to: Is there a social gradient in how youth with mental disorder perform academically? Findings from a Swedish longitudinal register-based study

The effectiveness of psychological support interventions for those exposed to mass infectious disease outbreaks: a systematic review

Acceptability of a digital return-to-work intervention for common mental disorders: a qualitative study on service user perspectives