Detecting neuroimaging biomarkers for schizophrenia: A meta-analysis of multivariate pattern recognition studies

Neuropsychopharmacology

Volumn 40, Issue 7, 2015, Pages 1742-1751

  Kambeitz, Joseph ^a,b   Kambeitz Ilankovic, Lana ^a   Leucht, Stefan ^c   Wood, Stephen ^d   Davatzikos, Christos ^e   Malchow, Berend ^a   Falkai, Peter ^a   Koutsouleris, Nikolaos ^a  

^a UNIVERSITY OF MUNICH   (Germany)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^d UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^e UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

BIBLIOGRAPHIC DATABASE; BRAIN; HUMAN; META ANALYSIS; METABOLISM; NEUROIMAGING; PATHOLOGY; PHYSIOLOGY; RECOGNITION; SCHIZOPHRENIA; STATISTICS AND NUMERICAL DATA;

BIOMARKERS; BRAIN; DATABASES, BIBLIOGRAPHIC; HUMANS; NEUROIMAGING; RECOGNITION (PSYCHOLOGY); SCHIZOPHRENIA;

EID: 84939987420     PISSN: 0893133X     EISSN: 1740634X     Source Type: Journal    
DOI: 10.1038/npp.2015.22     Document Type: Article

References (58)

1. Anderson A, Dinov ID, Sherin JE, Quintana J, Yuille AL, Cohen MS (2010). Classification of spatially unaligned fMRI scans. NeuroImage 49: 2509-2519.
2. Bansal R, Staib LH, Laine AF, Hao X, Xu D, Liu J et al (2012). Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses. PloS One 7: e50698.
3. Borgwardt S, Fusar-Poli P (2012). Third-generation neuroimaging in early schizophrenia: translating research evidence into clinical utility. Br J Psychiatry 200: 270-272.
4. Borgwardt S, Radua J, Mechelli A, Fusar-Poli P (2012). Why are psychiatric imaging methods clinically unreliable? Conclusions and practical guidelines for authors, editors and reviewers. Behav Brain Funct BBF 8: 46.
5. Bose SK, Turkheimer FE, Howes OD, Mehta MA, Cunliffe R, Stokes PR et al (2008). Classification of schizophrenic patients and healthy controls using [18 F] fluorodopa PET imaging. Schizophr Res 106: 148-155.
6. Castellani U, Rossato E, Murino V, Bellani M, Rambaldelli G, Perlini C et al (2012). Classification of schizophrenia using feature-based morphometry. J Neural Transm Vienna Austria 1996 119: 395-404.
7. Chan RCK, Di X, McAlonan GM, Gong Q-Y (2009). Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophr Bull 37: 177-188.
8. Costafreda SG, Fu CHY, Picchioni M, Toulopoulou T, McDonald C, Kravariti E et al (2011). Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry 11: 18.
9. Davatzikos C (2004). Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 23: 17-20.
10. Davatzikos C, Shen D, Gur RC, Wu X, Liu D, Fan Y et al (2005). Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch Gen Psychiatry 62: 1218-1227.
11. Deeks JJ, Macaskill P, Irwig L (2005). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58: 882-893.
12. Devillé WL, Buntinx F, Bouter LM, Montori VM, de Vet HC, van der Windt DA et al (2002). Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2: 9.
13. Doebler P (2012): Meta-Analysis of Diagnostic Accuracy with mada. Available at ohttp://cran.gis-lab.info/web/packages/mada/vignettes/ mada.pdf4. Last accessed date 1 August 2014.
14. Fan Y, Shen D, Gur RC, Gur RE, Davatzikos C (2007). COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 26: 93-105.
15. Fekete T, Wilf M, Rubin D, Edelman S, Malach R, Mujica-Parodi LR (2013). Combining classification with fMRI-derived complex network measures for potential neurodiagnostics. PloS One 8: e62867.
16. Filipovych R, Davatzikos CAlzheimer's Disease Neuroimaging Initiative (2011). Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage 55: 1109-1119.
17. Filipovych R, Resnick SM, Davatzikos C (2012). JointMMCC: joint maximum-margin classification and clustering of imaging data. IEEE Trans Med Imaging 31: 1124-1140.
18. Fornito A, Yücel M, Patti J, Wood SJ, Pantelis C (2009). Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies. Schizophr Res 108: 104-113.
19. Fu CHY, Costafreda SG (2013). Neuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift. Can J Psychiatry Rev Can Psychiatr 58: 499-508.
20. Fusar-Poli P, Smieskova R, Kempton MJ, Ho BC, Andreasen NC, Borgwardt S (2013). Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev 37: 1680-1691.
21. Gatsonis C, Paliwal P (2006). Meta-analysis of diagnostic and screening test accuracy evaluations: methodologic primer. AJR Am J Roentgenol 187: 271-281.
22. Greenstein D, Malley JD, Weisinger B, Clasen L, Gogtay N (2012). Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls. Front Psychiatry Front Res Found 3: 53.
23. Harbord RM, Deeks JJ, Egger M, Whiting P, Sterne JAC (2007). A unification of models for meta-analysis of diagnostic accuracy studies. Biostat Oxf Engl 8: 239-251.
24. Hastie T, Tibshirani R, Friedman J (2009). The elements of statistical learning. Springer, 2.
25. Ho B-C, Andreasen NC, Ziebell S, Pierson R, Magnotta V (2011). Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch Gen Psychiatry 68: 128-137.
26. Honea R, Crow TJ, Passingham D, Mackay CE (2005). Regional deficits in brain volume in schizophrenia: a meta-analysis of voxelbased morphometry studies. Am J Psychiatry 162: 2233-2245.
27. Howes OD, Kambeitz J, Kim E, Stahl D, Slifstein M, Abi-Dargham A et al (2012). The nature of dopamine dysfunction in schizophrenia and what this means for treatment: meta-analysis of imaging studies. Arch Gen Psychiatry 69: 776-786.
28. Hu X, Zhu D, Lv P, Li K, Han J, Wang L et al (2013). Fine-granularity functional interaction signatures for characterization of brain conditions. Neuroinformatics 11: 301-317.
29. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K et al (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 167: 748-751.
30. Ioannidis J, Lau J (2001). Evolution of treatment effects over time: empirical insight from recursive cumulative metaanalyses. Proc Natl Acad Sci USA 98: 831-836.
31. Josin GM, Liddle PF (2001). Neural network analysis of the pattern of functional connectivity between cerebral areas in schizophrenia. Biol Cybern 84: 117-122.
32. 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: 1174-1179.
33. Kawasaki Y, Suzuki M, Kherif F, Takahashi T, Zhou S-Y, Nakamura K et al (2007). Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls. NeuroImage 34: 235-242.
34. Koutsouleris N, Gaser C, Jäger M, Bottlender R, Frodl T, Holzinger S et al (2008). Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxel-based morphometric study. NeuroImage 39: 1600-1612.
35. Liu M, Zeng L-L, Shen H, Liu Z, Hu D (2012). Potential risk for healthy siblings to develop schizophrenia: evidence from pattern classification with whole-brain connectivity. Neuroreport 23: 265-269.
36. McGrath J, Saha S, Chant D, Welham J (2008). Schizophrenia: a concise overview of incidence, prevalence, and mortality. Epidemiol Rev 30: 67-76.
37. McIntosh AR, Lobaugh NJ (2004). Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23(Suppl 1): S250-S263.
38. Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC (2009). Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry 66: 811-822.
39. Modinos G, Costafreda SG, van Tol M-J, McGuire PK, Aleman A, Allen P (2013). Neuroanatomy of auditory verbal hallucinations in schizophrenia: a quantitative meta-analysis of voxel-based morphometry studies. Cortex 49: 1046-1055.
40. Moher D, Liberati A, Tetzlaff J, Altman DG (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339: b2535.
41. Navari S, Dazzan P (2009). Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychol Med 39: 1763-1777.
42. Nenadic I, Sauer H, Gaser C (2010). Distinct pattern of brain structural deficits in subsyndromes of schizophrenia delineated by psychopathology. NeuroImage 49: 1153-1160.
43. Nieuwenhuis M, Haren NEM, van, Hulshoff Pol HE, Cahn W, Kahn RS, Schnack HG (2012). Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. NeuroImage 61: 606-612.
44. Palaniyappan L, Balain V, Radua J, Liddle PF (2012). Structural correlates of auditory hallucinations in schizophrenia: a metaanalysis. Schizophr Res 137: 169-173.
45. Pers TH, Albrechtsen A, Holst C, Srensen TIA, Gerds TA (2009). The validation and assessment of machine learning: a game of prediction from high-dimensional data. PLoS One 4: e6287.
46. Rathi Y, Malcolm J, Michailovich O, Goldstein J, Seidman L, McCarley RW et al (2010). Biomarkers for identifying first-episode schizophrenia patients using diffusion weighted imaging. Med Image Comput Comput-Assist Interv 13: 657-665.
47. R Core Team (2013). R Foundation for Statistical Computing: Vienna, Austria, R: A Language and Environment for Statistical Computing. Available at ohttp://www.R-project.org/4.
48. Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58: 982-990.
49. Rutter CM, Gatsonis CA (2001). A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 20: 2865-2884.
50. Schnack HG, Nieuwenhuis M, Haren NEM, van, Abramovic L, Scheewe TW, Brouwer RM et al (2014). Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. NeuroImage 84: 299-306.
51. Shen H, Wang L, Liu Y, Hu D (2010). Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage 49: 3110-3121.
52. Smieskova R, Fusar-Poli P, Allen P, Bendfeldt K, Stieglitz RD, Drewe J et al (2009). The effects of antipsychotics on the brain: what have we learnt from structural imaging of schizophrenia? A systematic review. Curr Pharm Des 15: 2535-2549.
53. Tost H, Braus DF, Hakimi S, Ruf M, Vollmert C, Hohn F et al (2010). Acute D2 receptor blockade induces rapid, reversible remodeling in human cortical-striatal circuits. Nat Neurosci 13: 920-922.
54. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155: 529-536.
55. WHO (2004). Global Burden of Disease - Update. Available at ohttp://www.who.int/healthinfo/global-burden-disease/en4.
56. Zarogianni E, Moorhead TWJ, Lawrie SM (2013). Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. NeuroImage Clin 3: 279-289.
57. Zhang T, Koutsouleris N, Meisenzahl E, Davatzikos C (2014). Heterogeneity of structural brain changes in subtypes of schizophrenia revealed using magnetic resonance imaging pattern analysis. Schizophr Bull 41: 74-84.
58. Zwinderman A, Bossuyt P (2008). We should not pool diagnostic likelihood ratios in systematic reviews. Stat Med 27: 687-697.