Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies
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Citações na Scopus
107
Tipo de produção
article
Data de publicação
2017
Editora
ELSEVIER SCIENCE INC
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Título do Volume
Autores
KAMBEITZ, Joseph
CABRAL, Carlos
SACCHET, Matthew D.
GOTLIB, Ian H.
ZAHN, Roland
WALTER, Martin
FALKAI, Peter
KOUTSOULERIS, Nikolaos
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Organizadores
Citação
BIOLOGICAL PSYCHIATRY, v.82, n.5, p.330-338, 2017
Resumo
BACKGROUND: Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies. METHODS: We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs. RESULTS: Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the metaanalysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity). CONCLUSIONS: Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
Palavras-chave
Affective disorder, Classification, Diagnosis, Prediction, Sensitivity, Specificity
Referências
- Abler B, 2012, J NEUROSCI, V32, P1329, DOI 10.1523/JNEUROSCI.5826-11.2012
- Albert PR, 2013, PHILOS T R SOC B, V368, DOI 10.1098/rstb.2012.0535
- Beck AT, 1996, J PERS ASSESS, V67, P588, DOI 10.1207/s15327752jpa6703_13
- Borgwardt S, 2012, BEHAV BRAIN FUNCT, V8, DOI 10.1186/1744-9081-8-46
- Bromet E, 2011, BMC MED, V9, DOI 10.1186/1741-7015-9-90
- Cabral C, 2016, SCHIZOPHRENIA BULL, V42, pS110, DOI 10.1093/schbul/sbw053
- Cao H, 2014, NEUROIMAGE, V84, P888, DOI 10.1016/j.neuroimage.2013.09.013
- Cao LL, 2014, PSYCHIAT CLIN NEUROS, V68, P110, DOI 10.1111/pcn.12106
- Cipriani A, 2012, COCHRANE DB SYST REV, V7
- Craddock RC, 2009, MAGN RESON MED, V62, P1619, DOI 10.1002/mrm.22159
- Davatzikos C, 2004, NEUROIMAGE, V23, P17, DOI 10.1016/j.neuroimage.2004.05.010
- Deeks JJ, 2005, J CLIN EPIDEMIOL, V58, P882, DOI 10.1016/j.jclinepi.2005.01.016
- Diener C, 2012, NEUROIMAGE, V61, P677, DOI 10.1016/j.neuroimage.2012.04.005
- Doebler P, 2012, METAANALYSIS DIAGNOS
- Eaton WW, 1997, ARCH GEN PSYCHIAT, V54, P993
- Fang P, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0045972
- Fried EI, 2016, MOL PSYCHIATR, V21, P724, DOI 10.1038/mp.2015.199
- Friedman J, 2010, J STAT SOFTW, V33, P1
- Fu CHY, 2008, BIOL PSYCHIAT, V63, P656, DOI 10.1016/j.biopsych.2007.08.020
- Gong QY, 2011, NEUROIMAGE, V55, P1497, DOI 10.1016/j.neuroimage.2010.11.079
- Gryglewski G, 2014, J CEREBR BLOOD F MET, V34, P1096, DOI 10.1038/jcbfm.2014.82
- Guo H, 2014, NEURAL REGEN RES, V9, P153, DOI 10.4103/1673-5374.125344
- Guo H, 2012, NEUROREPORT, V23, P1006, DOI 10.1097/WNR.0b013e32835a650c
- Guo SX, 2013, BRAIN BEHAV, V3, P637, DOI 10.1002/brb3.173
- Habes I, 2013, NEUROIMAGE-CLIN, V2, P675, DOI 10.1016/j.nicl.2013.05.001
- Hahn T, 2011, ARCH GEN PSYCHIAT, V68, P361, DOI 10.1001/archgenpsychiatry.2010.178
- HAMILTON M, 1960, J NEUROL NEUROSUR PS, V23, P56, DOI 10.1136/jnnp.23.1.56
- Jie NF, 2015, IEEE T AUTON MENT DE, V7, P320, DOI 10.1109/TAMD.2015.2440298
- Johnston BA, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0132958
- Jung J, 2014, J AFFECT DISORDERS, V169, P179, DOI 10.1016/j.jad.2014.08.018
- Kaiser RH, 2015, JAMA PSYCHIAT, V72, P603, DOI 10.1001/jamapsychiatry.2015.0071
- Kambeitz J, 2015, NEUROPSYCHOPHARMACOL, V40, P1742, DOI 10.1038/npp.2015.22
- Kambeitz JP, 2015, J AFFECT DISORDERS, V186, P358, DOI 10.1016/j.jad.2015.07.034
- Kambeitz-Ilankovic L, 2016, SCHIZOPHR RES, V173, P159, DOI 10.1016/j.schres.2015.03.005
- Kapur S, 2012, MOL PSYCHIATR, V17, P1174, DOI 10.1038/mp.2012.105
- Kipli K, 2015, INT J COMPUT ASS RAD, V10, P1003, DOI 10.1007/s11548-014-1130-9
- Kloppel S, 2012, NEUROIMAGE, V61, P457, DOI 10.1016/j.neuroimage.2011.11.002
- Korgaonkar MS, 2014, BRIT J PSYCHIAT, V205, P321, DOI 10.1192/bjp.bp.113.140376
- Koutsouleris N, 2015, BRAIN, V138, P2059, DOI 10.1093/brain/awv111
- Koutsouleris N, 2014, SCHIZOPHRENIA BULL, V40, P1140, DOI 10.1093/schbul/sbt142
- Koutsouleris N, 2009, ARCH GEN PSYCHIAT, V66, P700, DOI 10.1001/archgenpsychiatry.2009.62
- Kuhn S., 2011, SCHIZOPHR B, V39, P358
- Liu F, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0040968
- Lois G, 2016, SOC COGN AFFECT NEUR, V11, P1792, DOI 10.1093/scan/nsw085
- Lord A, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0041282
- Lythe KE, 2015, JAMA PSYCHIAT, V72, P1119, DOI 10.1001/jamapsychiatry.2015.1813
- Ma QM, 2013, BRAIN RES, V1495, P86, DOI 10.1016/j.brainres.2012.12.002
- Manea L, 2015, GEN HOSP PSYCHIAT, V37, P67, DOI 10.1016/j.genhosppsych.2014.09.009
- Marquand AF, 2008, NEUROREPORT, V19, P1507, DOI 10.1097/WNR.0b013e328310425e
- Metzger CD, 2015, INT J NEUROPSYCHOPHA, V19
- Moher D, 2009, BMJ-BRIT MED J, V339, DOI 10.1136/bmj.b2535
- Mourao-Miranda J, 2012, PSYCHOL MED, V42, P1037, DOI 10.1017/S0033291711002005
- Mwangi B, 2012, BRAIN, V135, P1508, DOI 10.1093/brain/aws084
- Mwangi B, 2012, J MAGN RESON IMAGING, V35, P64, DOI 10.1002/jmri.22806
- Nouretdinov I, 2011, NEUROIMAGE, V56, P809, DOI 10.1016/j.neuroimage.2010.05.023
- Nugent AC, 2008, NEUROIMAGE, V43, P764, DOI 10.1016/j.neuroimage.2008.07.040
- Oiesvold T, 2013, BMC PSYCHIATRY, V13, DOI 10.1186/1471-244X-13-13
- Patel MJ, 2015, INT J GERIATR PSYCH, V30, P1056, DOI 10.1002/gps.4262
- Posner J, 2013, JAMA PSYCHIAT, V70, P373, DOI 10.1001/jamapsychiatry.2013.455
- Power JD, 2012, NEUROIMAGE, V59, P2142, DOI 10.1016/j.neuroimage.2011.10.018
- Qin J, 2015, NEUROREPORT, V26, P675, DOI 10.1097/WNR.0000000000000407
- Qin JL, 2015, J AFFECT DISORDERS, V180, P129, DOI 10.1016/j.jad.2015.03.059
- R Core Team, 2013, R LANG ENV STAT COMP
- Redlich R, 2014, JAMA PSYCHIAT, V71, P1222, DOI 10.1001/jamapsychiatry.2014.1100
- Regier DA, 2013, AM J PSYCHIAT, V170, P59, DOI 10.1176/appi.ajp.2012.12070999
- Reitsma JB, 2005, J CLIN EPIDEMIOL, V58, P982, DOI 10.1016/j.jclinepi.2005.02.022
- Rondina J. M., 2014, T MEDICAL IMAGING, V33, P85
- Rosa MJ, 2015, NEUROIMAGE, V105, P493, DOI 10.1016/j.neuroimage.2014.11.021
- Sacchet MD, 2015, J PSYCHIATR RES, V68, P91, DOI 10.1016/j.jpsychires.2015.06.002
- Sacchet MD, 2015, FRONT PSYCHIATRY, V6, DOI 10.3389/fpsyt.2015.00021
- Sacher J, 2012, J AFFECT DISORDERS, V140, P142, DOI 10.1016/j.jad.2011.08.001
- Sato JR, 2015, PSYCHIAT RES-NEUROIM, V233, P289, DOI 10.1016/j.pscychresns.2015.07.001
- Satterthwaite TD, 2012, NEUROIMAGE, V60, P623, DOI 10.1016/j.neuroimage.2011.12.063
- Schmaal L, 2016, MOL PSYCHIATR, V21, P806, DOI 10.1038/mp.2015.69
- Schnack HG, 2016, FRONT PSYCHIATRY, V7, DOI 10.3389/fpsyt.2016.00050
- Serpa MH, 2014, BIOMED RES INT, DOI 10.1155/2014/706157
- Sexton CE, 2009, BIOL PSYCHIAT, V66, P814, DOI 10.1016/j.biopsych.2009.05.024
- Shimizu Y, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0123524
- Siegle GJ, 2012, ARCH GEN PSYCHIAT, V69, P913, DOI 10.1001/archgenpsychiatry.2012.65
- Sui J, 2014, IEEE ENG MED BIO, P3889, DOI 10.1109/EMBC.2014.6944473
- Van Dijk KRA, 2012, NEUROIMAGE, V59, P431, DOI 10.1016/j.neuroimage.2011.07.044
- Wade BSC, 2015, I S BIOMED IMAGING, P92, DOI 10.1109/ISBI.2015.7163824
- Wei MB, 2013, PSYCHIAT RES-NEUROIM, V214, P306, DOI 10.1016/j.pscychresns.2013.09.008
- Wolfers T, 2015, NEUROSCI BIOBEHAV R, V57, P328, DOI 10.1016/j.neubiorev.2015.08.001
- Wu MJ, 2015, J PSYCHIATR RES, V62, P84, DOI 10.1016/j.jpsychires.2015.01.015
- Yang WJ, 2016, J AFFECT DISORDERS, V190, P880, DOI 10.1016/j.jad.2015.05.034
- Zeng LL, 2012, BRAIN, V135, P1498, DOI 10.1093/brain/aws059
- Zhang X, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0027253
- Zwinderman AH, 2008, STAT MED, V27, P687, DOI 10.1002/sim.2992