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Review
. 2022:36:103157.
doi: 10.1016/j.nicl.2022.103157. Epub 2022 Aug 17.

MRI predictors of pharmacotherapy response in major depressive disorder

Andrew R Gerlach  1 , Helmet T Karim  2 , Marta Peciña  1 , Olusola Ajilore  3 , Warren D Taylor  4 , Meryl A Butters  1 , Carmen Andreescu  5
Affiliations
Review

MRI predictors of pharmacotherapy response in major depressive disorder

Andrew R Gerlach et al. Neuroimage Clin. 2022.

Abstract

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.

Keywords: Antidepressant; MRI; Major depressive disorder; Prediction; Response.

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Conflict of interest statement

Declaration of Competing Interest Dr. Olusola Ajilore is the co-founder of KeyWise AI and serves on the advisory boards of Embodied Labs and Blueprint. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Brain map of volumetric predictors of treatment outcome with subjective scale. ACC = anterior cingulate cortex, CSF = cerebral spinal fluid, MFG = middle frontal gyrus. Created with BrainNet Viewer (Botvinik-Nezer et al., 2020).
Fig. 2
Fig. 2
Brain map of white matter tract predictors of treatment outcome. Higher FA of both the cingulum (left) and frontal (right) white matter tracts are associated with better treatment outcome. Created with DSI Studio (https://dsi-studio.labsolver.org/).
Fig. 3
Fig. 3
Resting state network fMRI predictors of treatment outcome with subjective scale. Within network connectivity is indicated by the network maps, between network connectivity is indicated by arrows. DMN = default mode network, ECN = executive control network, SN = salience network. Created with itk-SNAP (Yushkevich et al., 2006).
Fig. 4
Fig. 4
Brain map of task fMRI predictors of treatment outcome. Strength of effect is indicated by both color and size; color also indicates direction of effect. Created with BrainNet Viewer (Xia et al., 2013).
See this image and copyright information in PMC

References

    1. H.A. Whiteford A.J. Ferrari L. Degenhardt V. Feigin T. Vos G. Forloni The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010 PloS One 10 2 2015 e0116820 e0116820. - PMC - PubMed
    1. Murray C.J.L., Vos T., Lozano R., Naghavi M., Flaxman A.D., Michaud C., Ezzati M., Shibuya K., Salomon J.A., Abdalla S., Aboyans V., Abraham J., Ackerman I., Aggarwal R., Ahn S.Y., Ali M.K., AlMazroa M.A., Alvarado M., Anderson H.R., Anderson L.M., Andrews K.G., Atkinson C., Baddour L.M., Bahalim A.N., Barker-Collo S., Barrero L.H., Bartels D.H., Basáñez M.-G., Baxter A., Bell M.L., Benjamin E.J., Bennett D., Bernabé E., Bhalla K., Bhandari B., Bikbov B., Abdulhak A.B., Birbeck G., Black J.A., Blencowe H., Blore J.D., Blyth F., Bolliger I., Bonaventure A., Boufous S., Bourne R., Boussinesq M., Braithwaite T., Brayne C., Bridgett L., Brooker S., Brooks P., Brugha T.S., Bryan-Hancock C., Bucello C., Buchbinder R., Buckle G., Budke C.M., Burch M., Burney P., Burstein R., Calabria B., Campbell B., Canter C.E., Carabin H., Carapetis J., Carmona L., Cella C., Charlson F., Chen H., Cheng A.-A., Chou D., Chugh S.S., Coffeng L.E., Colan S.D., Colquhoun S., Colson K.E., Condon J., Connor M.D., Cooper L.T., Corriere M., Cortinovis M., de Vaccaro K.C., Couser W., Cowie B.C., Criqui M.H., Cross M., Dabhadkar K.C., Dahiya M., Dahodwala N., Damsere-Derry J., Danaei G., Davis A., Leo D.D., Degenhardt L., Dellavalle R., Delossantos A., Denenberg J., Derrett S., Des Jarlais D.C., Dharmaratne S.D., Dherani M., Diaz-Torne C., Dolk H., Dorsey E.R., Driscoll T., Duber H., Ebel B., Edmond K., Elbaz A., Ali S.E., Erskine H., Erwin P.J., Espindola P., Ewoigbokhan S.E., Farzadfar F., Feigin V., Felson D.T., Ferrari A., Ferri C.P., Fèvre E.M., Finucane M.M., Flaxman S., Flood L., Foreman K., Forouzanfar M.H., Fowkes F.G.R., Fransen M., Freeman M.K., Gabbe B.J., Gabriel S.E., Gakidou E., Ganatra H.A., Garcia B., Gaspari F., Gillum R.F., Gmel G., Gonzalez-Medina D., Gosselin R., Grainger R., Grant B., Groeger J., Guillemin F., Gunnell D., Gupta R., Haagsma J., Hagan H., Halasa Y.A., Hall W., Haring D., Haro J.M., Harrison J.E., Havmoeller R., Hay R.J., Higashi H., Hill C., Hoen B., Hoffman H., Hotez P.J., Hoy D., Huang J.J., Ibeanusi S.E., Jacobsen K.H., James S.L., Jarvis D., Jasrasaria R., Jayaraman S., Johns N., Jonas J.B., Karthikeyan G., Kassebaum N., Kawakami N., Keren A., Khoo J.-P., King C.H., Knowlton L.M., Kobusingye O., Koranteng A., Krishnamurthi R., Laden F., Lalloo R., Laslett L.L., Lathlean T., Leasher J.L., Lee Y.Y., Leigh J., Levinson D., Lim S.S., Limb E., Lin J.K., Lipnick M., Lipshultz S.E., Liu W., Loane M., Ohno S.L., Lyons R., Mabweijano J., MacIntyre M.F., Malekzadeh R., Mallinger L., Manivannan S., Marcenes W., March L., Margolis D.J., Marks G.B., Marks R., Matsumori A., Matzopoulos R., Mayosi B.M., McAnulty J.H., McDermott M.M., McGill N., McGrath J., Medina-Mora M.E., Meltzer M., Memish Z.A., Mensah G.A., Merriman T.R., Meyer A.-C., Miglioli V., Miller M., Miller T.R., Mitchell P.B., Mock C., Mocumbi A.O., Moffitt T.E., Mokdad A.A., Monasta L., Montico M., Moradi-Lakeh M., Moran A., Morawska L., Mori R., Murdoch M.E., Mwaniki M.K., Naidoo K., Nair M.N., Naldi L., Narayan K.M.V., Nelson P.K., Nelson R.G., Nevitt M.C., Newton C.R., Nolte S., Norman P., Norman R., O'Donnell M., O'Hanlon S., Olives C., Omer S.B., Ortblad K., Osborne R., Ozgediz D., Page A., Pahari B., Pandian J.D., Rivero A.P., Patten S.B., Pearce N., Padilla R.P., Perez-Ruiz F., Perico N., Pesudovs K., Phillips D., Phillips M.R., Pierce K., Pion S., Polanczyk G.V., Polinder S., Pope C.A., Popova S., Porrini E., Pourmalek F., Prince M., Pullan R.L., Ramaiah K.D., Ranganathan D., Razavi H., Regan M., Rehm J.T., Rein D.B., Remuzzi G., Richardson K., Rivara F.P., Roberts T., Robinson C., De Leòn F.R., Ronfani L., Room R., Rosenfeld L.C., Rushton L., Sacco R.L., Saha S., Sampson U., Sanchez-Riera L., Sanman E., Schwebel D.C., Scott J.G., Segui-Gomez M., Shahraz S., Shepard D.S., Shin H., Shivakoti R., Silberberg D., Singh D., Singh G.M., Singh J.A., Singleton J., Sleet D.A., Sliwa K., Smith E., Smith J.L., Stapelberg N.JC., Steer A., Steiner T., Stolk W.A., Stovner L.J., Sudfeld C., Syed S., Tamburlini G., Tavakkoli M., Taylor H.R., Taylor J.A., Taylor W.J., Thomas B., Thomson W.M., Thurston G.D., Tleyjeh I.M., Tonelli M., Towbin J.A., Truelsen T., Tsilimbaris M.K., Ubeda C., Undurraga E.A., van der Werf M.J., van Os J., Vavilala M.S., Venketasubramanian N., Wang M., Wang W., Watt K., Weatherall D.J., Weinstock M.A., Weintraub R., Weisskopf M.G., Weissman M.M., White R.A., Whiteford H., Wiebe N., Wiersma S.T., Wilkinson J.D., Williams H.C., Williams S.RM., Witt E., Wolfe F., Woolf A.D., Wulf S., Yeh P.-H., Zaidi A.KM., Zheng Z.-J., Zonies D., Lopez A.D. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012;380(9859):2197–2223. - PubMed
    1. Brenes G.A. Anxiety, Depression, and Quality of Life in Primary Care Patients. Prim. Care Companion J. Clin. Psychiatry. 2007;9:437–443. - PMC - PubMed
    1. Greenberg P.E., Fournier A.-A., Sisitsky T., Pike C.T., Kessler R.C. The economic burden of adults with major depressive disorder in the United States (2005 and 2010) J. Clin. Psychiatry. 2015;76:155–162. - PubMed
    1. Strakowski S., Nelson E. Oxford University Press; 2015. Major Depressive Disorder.