MRI predictors of pharmacotherapy response in major depressive disorder

Gerlach, Andrew R.; Karim, Helmet T.; Peciña, Marta; Ajilore, Olusola; Taylor, Warren D.; Butters, Meryl A.; Andreescu, Carmen

doi:10.1016/j.nicl.2022.103157

Cited by 57Treatment of Major DepressionFunctional Brain Connectivity StudiesNeural and Behavioral Psychology Studies

MRI predictors of pharmacotherapy response in major depressive disorder

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Gerlach, Andrew R., Karim, Helmet T., Peciña, Marta, Ajilore, Olusola, Taylor, Warren D., Butters, Meryl A., Andreescu, Carmen·2022·NeuroImage: Clinical

DOI 10.1016/j.nicl.2022.103157PMC PMC9420953

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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.

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Amygdala reactivity to negative emotions is reduced or normalized by antidepressant medication.
“that is reduced by an depressant medica on.”
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Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812 · see citing works
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
Murray, C. J. L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61689-4 · Cited by 8,888 · see citing works
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability.
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · see citing works
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676 · see citing works
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works
Saliency, switching, attention and control: a network model of insula function
Menon, V., & Uddin, L. Q. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0262-0 · Cited by 5,592 · see citing works
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150 · see citing works
Large-scale brain networks and psychopathology: a unifying triple network model
Menon, V. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.08.003 · Cited by 4,168 · see citing works
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106 · see citing works
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487 · see citing works
To Explain or to Predict?
Galit Shmueli, A. (2010) · Statistical Science · doi:10.1214/10-sts330 · Cited by 3,121 · see citing works
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750 · see citing works
Diffusion tensor imaging of the brain
Alexander, A. L., Lee, J. E., Lazar, M., & Field, A. S. (2007) · Neurotherapeutics · doi:10.1016/j.nurt.2007.05.011 · Cited by 2,747 · see citing works
Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., & Schatzberg, A. F. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.09.020 · Cited by 2,214 · see citing works
'Vascular Depression' Hypothesis
Alexopoulos, G. S. (1997) · Archives of General Psychiatry · doi:10.1001/archpsyc.1997.01830220033006 · Cited by 1,843 · see citing works
The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010)
Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.14m09298 · Cited by 1,779 · see citing works
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580 · see citing works
Self-citation
Increased amygdala response to masked emotional faces in depressed subjects resolves with antidepressant treatment: an fMRI study
Sheline, Y. I., Barch, D. M., Donnelly, J. M., Ollinger, J. M., Snyder, A. Z., & Mintun, M. A. (2001) · Biological Psychiatry · doi:10.1016/s0006-3223(01)01263-x · Cited by 1,191 · see citing works
The global burden of mental, neurological and substance use disorders: An analysis from the Global Burden of Disease Study 2010.
Whiteford, H. A., Ferrari, A. J., Degenhardt, L., Feigin, V., & Vos, T. (2015) · PLOS ONE · doi:10.1371/journal.pone.0116820 · Cited by 1,175 · see citing works
Variability in the analysis of a single neuroimaging dataset by many teams
(2020) · doi:10.1038/s41586-020-2314-9 · Cited by 1,097 · see citing works
Frontocingulate Dysfunction in Depression: Toward Biomarkers of Treatment Response
Pizzagalli, D. A. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2010.166 · Cited by 945 · see citing works
The vascular depression hypothesis: mechanisms linking vascular disease with depression
Taylor, W. D., Aizenstein, H. J., & Alexopoulos, G. S. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.20 · Cited by 923 · see citing works
Self-citation
The cingulate cortex and limbic systems for emotion, action, and memory
Rolls, E. T. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01945-2 · Cited by 859 · see citing works
DEFINITION AND EPIDEMIOLOGY OF TREATMENT-RESISTANT DEPRESSION
Fava, M., & Davidson, K. G. (1996) · Psychiatric Clinics of North America · doi:10.1016/s0193-953x(05)70283-5 · Cited by 797 · see citing works
Attenuation of the Neural Response to Sad Faces in Major Depressionby Antidepressant Treatment: A Prospective, Event-Related Functional Magnetic Resonance ImagingStudy
Fu, C. H. Y., Williams, S. C. R., Cleare, A. J., Brammer, M. J., Walsh, N. D., Kim, J., Andrew, C. M., Pich, E. M., Williams, P. M., Reed, L. J., et al. (2004) · Archives of General Psychiatry · doi:10.1001/archpsyc.61.9.877 · Cited by 775 · see citing works
MRI-defined vascular depression
(1997) · American Journal of Psychiatry · doi:10.1176/ajp.154.4.497 · Cited by 770 · see citing works
Towards a universal taxonomy of macro-scale functional human brain networks
Uddin, L. Q., Yeo, B. T. T., & Spreng, R. N. (2019) · Brain Topography · doi:10.1007/s10548-019-00744-6 · Cited by 683 · see citing works
Precision psychiatry: a neural circuit taxonomy for depression and anxiety
Williams, L. M. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00579-9 · Cited by 641 · see citing works
Graph-based network analysis of resting-state functional MRI
Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614 · see citing works
Age differences in major depression: Results from the national comorbidity survey replication (NCS-R)
Kessler, R. C., Birnbaum, H., Bromet, E., Hwang, I., Sampson, N., & Shahly, V. (2010) · Psychological Medicine · doi:10.1017/s0033291709990213 · Cited by 542 · see citing works
The neural substrates of affective processing in depressed patients treated with venlafaxine
Davidson, R. J., Irwin, W., Anderle, M. J., & Kalin, N. H. (2003) · American Journal of Psychiatry · doi:10.1176/appi.ajp.160.1.64 · Cited by 487 · see citing works
The principled control of false positives in neuroimaging
Bennett, C. M., Wolford, G. L., & Miller, M. B. (2009) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsp053 · Cited by 486 · see citing works
Functional connectivity in the cognitive control network and the default mode network in late-life depression
Alexopoulos, G. S., Hoptman, M. J., Kanellopoulos, D., Murphy, C. F., Lim, K. O., & Gunning, F. M. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.12.002 · Cited by 437 · see citing works
Meta-analysis of functional neuroimaging data: current and future directions
Wager, T. D., Lindquist, M., & Kaplan, L. (2007) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsm015 · Cited by 434 · see citing works
Independent component analysis of functional MRI: what is signal and what is noise?
McKeown, M. J., Hansen, L. K., & Sejnowsk, T. J. (2003) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2003.09.012 · Cited by 414 · see citing works
Relationship Between Amygdala Responses to Masked Faces and Mood State and Treatment in Major Depressive Disorder
Victor, T. A., Furey, M. L., Fromm, S. J., Öhman, A., & Drevets, W. C. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.144 · Cited by 400 · see citing works
Predictive neural biomarkers of clinical response in depression. a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies
Fu, C. H. Y., Steiner, H., & Costafreda, S. G. (2013) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.05.008 · Cited by 399 · see citing works
On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments
Carp, J. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00149 · Cited by 381 · see citing works
Ten simple rules for predictive modeling of individual differences in neuroimaging
Scheinost, D., Noble, S., Horien, C., Greene, A. S., Lake, E. M., Salehi, M., Gao, S., Shen, X., O'Connor, D., Barron, D. S., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.057 · Cited by 381 · see citing works
Hippocampal volume in primary unipolar major depression: a magnetic resonance imaging study
Vakili, K., Pillay, S. S., Lafer, B., D, M. F. M., Renshaw, P. F., Bonello, C. M., & Yurgelun-Todd, D. A. (2000) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00296-6 · Cited by 360 · see citing works
Brain Imaging Correlates of Depressive Symptom Severity and Predictors of Symptom Improvement After Antidepressant Treatment
Chen, C.-H., Ridler, K., Suckling, J., Williams, S., Fu, C. H. Y., Merlo-Pich, E., & Bullmore, E. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.09.018 · Cited by 354 · see citing works
Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive- behavioral therapy or antidepressant medication for major depressive disorder
Dunlop, B. W., Rajendra, J. K., Craighead, W. E., Kelley, M. E., McGrath, C. L., Choi, K. S., Kinkead, B., Nemeroff, C. B., & Mayberg, H. S. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.16050518 · Cited by 342 · cited 1× · see citing works
“Successful treatment has been associated with lower baseline connectivity between the subgenual ACC and the dorsal ACC (small study) (Kozel et al., 2011), ventromedial PFC (large study) (Dunlop, 2017), and PCC (Goldstein-Piekarski et al., 2018).”
Background
A Treatment-Resistant Default Mode Subnetwork in Major Depression
Li, B., Liu, L., Friston, K. J., Shen, H., Wang, L., Zeng, L.-L., & Hu, D. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.11.007 · Cited by 333 · see citing works
Depression, antidepressants, and the shrinking hippocampus
Sapolsky, R. M. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.231475998 · Cited by 327 · see citing works
Frontal White Matter Microstructure and Treatment Response of Late-Life Depression: A Preliminary Study
Alexopoulos, G. S., Kiosses, D. N., Choi, S. J., Murphy, C. F., & Lim, K. O. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.11.1929 · Cited by 307 · see citing works
Microstructural White Matter Abnormalities and Remission of Geriatric Depression
Alexopoulos, G. S., Murphy, C. F., Gunning‐Dixon, F. M., Latoussakis, V., Kanellopoulos, D., Klimstra, S., Lim, K. O., & Hoptman, M. J. (2008) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2007.07050744 · Cited by 297 · see citing works
Support for the Vascular Depression Hypothesis in Late-Life Depression: Results of a 2-Site, Prospective, Antidepressant Treatment Trial
Sheline, Y. I., Pieper, C. F., Barch, D. M., Welsh-Boehmer, K., McKinstry, R. C., MacFall, J. R., D’Angelo, G., Garcia, K. S., Gersing, K., Wilkins, C., et al. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2009.204 · Cited by 291 · see citing works
Self-citation
Hyporeactivity of ventral striatum towards incentive stimuli in unmedicated depressed patients normalizes after treatment with escitalopram
Stoy, M., Schlagenhauf, F., Sterzer, P., Bermpohl, F., Hägele, C., Suchotzki, K., Schmack, K., Wrase, J., Ricken, R., Knutson, B., et al. (2012) · Journal of Psychopharmacology · doi:10.1177/0269881111416686 · Cited by 279 · see citing works
Posterior hippocampal volumes are associated with remission rates in patients with major depressive disorder
MacQueen, G. M., Yucel, K., Taylor, V. H., Macdonald, K., & Joffe, R. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.06.027 · Cited by 274 · see citing works
Antidepressants Normalize the Default Mode Network in Patients With Dysthymia
Posner, J., Hellerstein, D. J., Gat, I., Mechling, A., Klahr, K., Wang, Z., McGrath, P. J., Stewart, J. W., & Peterson, B. S. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.455 · Cited by 268 · see citing works
Antidepressant treatment normalizes hypoactivity in dorsolateral prefrontal cortex during emotional interference processing in major depression
Fales, C. L., Barch, D. M., Rundle, M. M., Mintun, M. A., Mathews, J., Snyder, A. Z., & Sheline, Y. I. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2008.04.027 · Cited by 266 · see citing works
Anxiety, Depression, and Quality of Life in Primary Care Patients
Brenes, G. A. (2007) · The Primary Care Companion to The Journal of Clinical Psychiatry · doi:10.4088/pcc.v09n0606 · Cited by 243 · see citing works
Prognostic and diagnostic potential of the structural neuroanatomy of depression
Costafreda, S. G., Chu, C., Ashburner, J., & Fu, C. H. Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006353 · Cited by 232 · see citing works
Hippocampal and amygdala changes in patients with major depressive disorder and healthy controls during a 1-year follow-up
Frodl, T., Meisenzahl, E. M., Zetzsche, T., Höhne, T., Banac, S., Schorr, C., Jäger, M., Leinsinger, G., Bottlender, R., Reiser, M., et al. (2004) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.v65n0407 · Cited by 227 · see citing works
Severe deep white matter lesions and outcome in elderly patients with major depressive disorder: follow up study
O'Brien, J. T., Ames, D. J., Chiu, E., Schweitzer, I., Desmond, P. M., & Tress, B. M. (1998) · BMJ · doi:10.1136/bmj.317.7164.982 · Cited by 226 · see citing works
Paradoxical effects of adrenal steroids on the brain: Protection versus degeneration
McEwen, B. S., Angulo, J. A., Cameron, H. A., Chao, H. M., Daniels, D. C., Gannon, M. N., Gould, E., Mendelson, S. D., Sakai, R. R., Spencer, R. L., et al. (1992) · Biological Psychiatry · doi:10.1016/0006-3223(92)90204-d · Cited by 219 · see citing works
Reciprocal effects of antidepressant treatment on activity and connectivity of the mood regulating circuit: an fMRI study
Anand, A., Li, Y., Wang, Y., Gardner, K., & Lowe, M. J. (2007) · The Journal of Neuropsychiatry and Clinical Neurosciences · doi:10.1176/appi.neuropsych.19.3.274 · Cited by 215 · see citing works
Structural and cognitive deficits in remitting and non-remitting recurrent depression: A voxel-based morphometric study
Li, C., Lin, C., Chou, K., Chen, I.-Y., Hsieh, J., Wu, C., Lin, W.-C., & Su, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.021 · Cited by 214 · see citing works
Frontal and Limbic Activation During Inhibitory Control Predicts Treatment Response in Major Depressive Disorder
Langenecker, S. A., Kennedy, S. E., Guidotti, L. M., Briceño, E. M., Own, L. S., Hooven, T. A., Young, E. A., Akil, H., Noll, D. C., & Zubieta, J. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2007.02.019 · Cited by 210 · see citing works
Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial
Williams, L. M., Korgaonkar, M. S., Song, Y. C., Paton, R., Eagles, S., Goldstein-Piekarski, A., Grieve, S. M., Harris, A. W. F., Usherwood, T., & Etkin, A. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.89 · Cited by 195 · see citing works
A fully automated method for quantifying and localizing white matter hyperintensities on MR images
Wu, M., Rosano, C., Butters, M. A., Whyte, E. M., Nable, M., Crooks, R., MELTZER, C. C., Reynolds, C. F., & Aizenstein, H. J. (2006) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2006.09.003 · Cited by 194 · see citing works
Self-citation
International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol
Williams, L. M., Rush, A. J., Koslow, S. H., Wisniewski, S. R., Cooper, N. J., Nemeroff, C. B., Schatzberg, A. F., & Gordon, E. (2011) · Trials · doi:10.1186/1745-6215-12-4 · Cited by 193 · see citing works
Increased amygdala responses to sad but not fearful faces in major depression: relation to mood state and pharmacological treatment
Arnone, D., McKie, S., Elliott, R., Thomas, E. J., Downey, D., Juhasz, G., Williams, S. R., Deakin, J. F. W., & Anderson, I. M. (2012) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.11121774 · Cited by 192 · cited 2× · see citing works
“…regardless of clinical outcome (Victor et al., 2010), similarly outcome-agnostic decreases in amygdala activation to sad but not fearful faces (Arnone, 2012), increases in amygdala activation to subliminal happy and threatening faces for responders and decreases in amygdala activation to…”
Background
Altered Functioning of the Executive Control Circuit in Late-Life Depression: Episodic and Persistent Phenomena
Aizenstein, H. J., Butters, M. A., Wu, M., Mazurkewicz, L. M., Stenger, V. A., Gianaros, P. J., Becker, J. T., Reynolds, C. F., & Carter, C. S. (2009) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e31817b60af · Cited by 187 · see citing works
Self-citation
Neuroimaging biomarkers as predictors of treatment outcome in Major Depressive Disorder
Fonseka, T. M., MacQueen, G. M., & Kennedy, S. H. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.10.049 · Cited by 185 · see citing works
Early changes in emotional processing as a marker of clinical response to SSRI treatment in depression
Godlewska, B. R., Browning, M., Norbury, R., Cowen, P. J., & Harmer, C. J. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.130 · Cited by 181 · see citing works
The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder
Wang, L., Xia, M., Li, K., Zeng, Y., Su, Y., Dai, W., Zhang, Q., Jin, Z., Mitchell, P. B., Yu, X., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22663 · Cited by 166 · see citing works
Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder
Korgaonkar, M. S., Goldstein-Piekarski, A. N., Fornito, A., & Williams, L. M. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0574-2 · Cited by 163 · see citing works
Exploring the impact of analysis software on task fMRI results
Bowring, A., Maumet, C., & Nichols, T. E. (2019) · Human Brain Mapping · doi:10.1002/hbm.24603 · Cited by 158 · see citing works
Resting state functional connectivity and treatment response in late-life depression
Andreescu, C., Tudorascu, D. L., Butters, M. A., Tamburo, E., Patel, M., Price, J., Karp, J. F., Reynolds, C. F., & Aizenstein, H. (2013) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2013.08.007 · Cited by 153 · see citing works
Self-citation
Frontoparietal Activation During Response Inhibition Predicts Remission to Antidepressants in Patients With Major Depression
Gyurak, A., Patenaude, B., Korgaonkar, M. S., Grieve, S. M., Williams, L. M., & Etkin, A. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.02.037 · Cited by 149 · see citing works
Neural markers of symptomatic improvement during antidepressant therapy in severe depression: subgenual cingulate and visual cortical responses to sad, but not happy, facial stimuli are correlated with changes in symptom score
Keedwell, P., Drapier, D., Surguladze, S., Giampietro, V, Brammer, M., & Phillips, M. (2009) · Journal of Psychopharmacology · doi:10.1177/0269881108093589 · Cited by 149 · see citing works
Response Inhibition Predicts Poor Antidepressant Treatment Response in Very Old Depressed Patients
Sneed, J. R., Roose, S. P., Keilp, J. G., Krishnan, K. R. R., Alexopoulos, G. S., & Sackeim, H. A. (2007) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e3180302513 · Cited by 146 · see citing works
Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder
Korgaonkar, M. S., Williams, L. M., Song, Y. J. C., Usherwood, T. P., & Grieve, S. M. (2014) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.113.140376 · Cited by 145 · see citing works
Default-mode network connectivity and white matter burden in late-life depression
Wu, M., Andreescu, C., Butters, M. A., Tamburo, R., Reynolds, C. F., & Aizenstein, H. (2011) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2011.04.003 · Cited by 140 · see citing works
Self-citation
Hippocampal volume and antidepressant response in geriatric depression
Hsieh, M., McQuoid, D. R., Levy, R. M., Payne, M. E., MacFall, J. R., & Steffens, D. C. (2002) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.611 · Cited by 136 · see citing works
Hippocampus atrophy and the longitudinal course of late-life depression
Taylor, W. D., McQuoid, D. R., Payne, M. E., Zannas, A. S., MacFall, J. R., & Steffens, D. C. (2014) · The American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2013.11.004 · Cited by 134 · see citing works
Self-citation
Neuroanatomy of verbal working memory as a diagnostic biomarker for depression
Marquand, A. F., Mourão-Miranda, J., Brammer, M. J., Cleare, A. J., & Fu, C. H. Y. (2008) · Neuroreport · doi:10.1097/wnr.0b013e328310425e · Cited by 125 · see citing works
Effect of intrinsic patterns of functional brain connectivity in moderating antidepressant treatment response in major depression
Chin Fatt, C. R., Jha, M. K., Cooper, C. M., Fonzo, G., South, C., Grannemann, B., Carmody, T., Greer, T. L., Kurian, B., Fava, M., et al. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18070870 · Cited by 125 · see citing works
Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers
Goldstein-Piekarski, A. N., Staveland, B. R., Ball, T. M., Yesavage, J., Korgaonkar, M. S., & Williams, L. M. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0100-3 · Cited by 108 · see citing works
Anterior cingulate cortical volumes and treatment remission of geriatric depression
Gunning, F. M., Cheng, J., Murphy, C. F., Kanellopoulos, D., Acuna, J., Hoptman, M. J., Klimstra, S., Morimoto, S., Weinberg, J., & Alexopoulos, G. S. (2009) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2290 · Cited by 106 · see citing works
Subgenual cingulate and visual cortex responses to sad faces predict clinical outcome during antidepressant treatment for depression☆
Keedwell, P. A., Drapier, D., Surguladze, S., Giampietro, V., Brammer, M., & Phillips, M. (2010) · Journal of Affective Disorders · doi:10.1016/j.jad.2009.04.031 · Cited by 106 · see citing works
Treatment response in late-onset depression: relationship to neuropsychological, neuroradiological and vascular risk factors
Baldwin, R., Jeffries, S., Jackson, A., Sutcliffe, C., Thacker, N. A., Scott, M. L. J., & Burns, A. S. (2004) · Psychological Medicine · doi:10.1017/s0033291703008870 · Cited by 105 · see citing works
Frontal White Matter Anisotropy and Antidepressant Remission in Late-Life Depression
Taylor, W. D., Kuchibhatla, M. N., Payne, M. E., MacFall, J. R., Sheline, Y. I., Krishnan, K. R., & Doraiswamy, P. M. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003267 · Cited by 101 · see citing works
Self-citation
Predicting Treatment Response in Depression: The Role of Anterior Cingulate Cortex
Godlewska, B. R., Browning, M. D., Norbury, R., Igoumenou, A., Cowen, P. J., & Harmer, C. J. (2018) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyy069 · Cited by 97 · see citing works
Antidepressant effects of sertraline associated with volume increases in dorsolateral prefrontal cortex
Smith, R., Chen, K., Baxter, L., Fort, C., & Lane, R. D. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.07.029 · Cited by 95 · see citing works
Intrinsic functional connectivity in late-life depression: trajectories over the course of pharmacotherapy in remitters and non-remitters
Karim, H. T., Andreescu, C., Tudorascu, D., Smagula, S. F., Butters, M. A., Karp, J. F., Reynolds, C., & Aizenstein, H. J. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.55 · Cited by 95 · see citing works
Self-citation
Magnetic Resonance Imaging Measures of Brain Structure to Predict Antidepressant Treatment Outcome in Major Depressive Disorder
Korgaonkar, M. S., Rekshan, W. R., Gordon, E., Rush, A. J., Williams, L. M., Blasey, C. M., & Grieve, S. M. (2014) · EBioMedicine · doi:10.1016/j.ebiom.2014.12.002 · Cited by 90 · see citing works
Increased hippocampal tail volume predicts depression status and remission to anti-depressant medications in major depression
Maller, J. J., Broadhouse, K., Rush, A. J., Gordon, E., Koslow, S., & Grieve, S. M. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.224 · Cited by 87 · see citing works
Treatment Course With Antidepressant Therapy in Late-Life Depression
Sheline, Y. I., Disabato, B. M., Hranilovich, J., Morris, C., D’Angelo, G., Pieper, C., Toffanin, T., Taylor, W. D., MacFall, J. R., Wilkins, C., et al. (2012) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12010122 · Cited by 86 · see citing works
Self-citation
Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression
Sikora, M., Heffernan, J., Avery, E. T., Mickey, B. J., Zubieta, J.-K., & Peciña, M. (2016) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.10.002 · Cited by 85 · see citing works
Self-citation
MRI signal hyperintensities and treatment remission of geriatric depression
Gunning‐Dixon, F. M., Walton, M. F., Cheng, J., Acuna, J., Klimstra, S., Zimmerman, M. E., Brickman, A. M., Hoptman, M. J., Young, R. C., & Alexopoulos, G. S. (2010) · Journal of Affective Disorders · doi:10.1016/j.jad.2010.04.004 · Cited by 83 · see citing works
Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine
Fu, C. H. Y., Costafreda, S. G., Sankar, A., Adams, T. M., Rasenick, M. M., Liu, P., Donati, R. J., Maglanoc, L. A., Horton, P., & Marangell, L. B. (2015) · BMC Psychiatry · doi:10.1186/s12888-015-0457-2 · Cited by 82 · see citing works
Differential effects of serotonergic and noradrenergic antidepressants on brain activity during a cognitive control task and neurofunctional prediction of treatment outcome in patients with depression
Wagner, G., Koch, K., Schachtzabel, C., Sobanski, T., Reichenbach, J. R., Sauer, H., & Schlösser, R. G. M. (2010) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.090081 · Cited by 80 · see citing works
Brain activation predicts treatment improvement in patients with major depressive disorder
Samson, A. C., Meisenzahl, E., Scheuerecker, J., Rose, E., Schoepf, V., Wiesmann, M., & Frodl, T. (2011) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2011.03.009 · Cited by 78 · see citing works
Clinical Presentation and Outcome of Geriatric Depression in Subcortical Ischemic Vascular Disease
Bella, R., Pennisi, G., Cantone, M., Palermo, F., Pennisi, M., Lanza, G., Zappia, M., & Paolucci, S. (2010) · Gerontology · doi:10.1159/000272003 · Cited by 78 · see citing works
COGNITION-CHILDHOOD MALTREATMENT INTERACTIONS IN THE PREDICTION OF ANTIDEPRESSANT OUTCOMES IN MAJOR DEPRESSIVE DISORDER PATIENTS: RESULTS FROM THE iSPOT-D TRIAL: Research Article: Cognition and Childhood Maltreatment in MDD
Miller, S., McTeague, L. M., Gyurak, A., Patenaude, B., Williams, L. M., Grieve, S. M., Korgaonkar, M. S., & Etkin, A. (2015) · Depression and Anxiety · doi:10.1002/da.22368 · Cited by 74 · see citing works
Neuroanatomical correlates of apathy in late-life depression and antidepressant treatment response
Yuen, G. S., Gunning, F. M., Woods, E. C., Klimstra, S., Hoptman, M. J., & Alexopoulos, G. S. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.05.008 · Cited by 73 · see citing works
Pretreatment and early-treatment cortical thickness is associated with SSRI treatment response in major depressive disorder
Bartlett, E. A., DeLorenzo, C., Sharma, P., Yang, J., Zhang, M., Petkova, E., Weissman, M., McGrath, P. J., Fava, M., Ogden, R. T., et al. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0122-9 · Cited by 72 · see citing works
Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI
Crane, N. A., Jenkins, L. M., Bhaumik, R., Dion, C., Gowins, J. R., Mickey, B. J., Zubieta, J., & Langenecker, S. A. (2017) · Brain · doi:10.1093/brain/aww326 · Cited by 71 · see citing works
Are there meaningful biomarkers of treatment response for depression?
Breitenstein, B., Scheuer, S., & Holsboer, F. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.02.002 · Cited by 69 · see citing works
Depression: The Shroud of Heterogeneity
Monroe, S. M., & Anderson, S. F. (2015) · Current Directions in Psychological Science · doi:10.1177/0963721414568342 · Cited by 68 · see citing works
Default mode network deactivation during emotion processing predicts early antidepressant response
Spies, M., Kraus, C., Geissberger, N., Auer, B., Klöbl, M., Tik, M., Stürkat, I.-L., Hahn, A., Woletz, M., Pfabigan, D. M., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2016.265 · Cited by 68 · see citing works
Different Effects of Mirtazapine and Venlafaxine on Brain Activation
Frodl, T., Scheuerecker, J., Schoepf, V., Linn, J., Koutsouleris, N., Bokde, A. L. W., Hampel, H. J., Möller, H., Brückmann, H., Wiesmann, M., et al. (2010) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.09m05393blu · Cited by 62 · see citing works
fMRI response to negative words and SSRI treatment outcome in major depressive disorder: A preliminary study
Miller, J. M., Schneck, N., Siegle, G. J., Chen, Y., Ogden, R. T., Kikuchi, T., Oquendo, M. A., Mann, J. J., & Parsey, R. V. (2013) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2013.08.001 · Cited by 62 · see citing works
Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report
Nogovitsyn, N., Muller, M., de Souza, R. M., Hassel, S., Arnott, S. R., Davis, A. D., Hall, G. B., Harris, J. K., Zamyadi, M., Metzak, P. D., et al. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0542-1 · Cited by 62 · see citing works
Virchow–Robin space dilatation may predict resistance to antidepressant monotherapy in elderly patients with depression
Patankar, T. F., Baldwin, R., Mitra, D., Jeffries, S., Sutcliffe, C., Burns, A. S., & Jackson, A. (2006) · Journal of Affective Disorders · doi:10.1016/j.jad.2006.06.024 · Cited by 61 · see citing works
Functional Connectivity of Brain Structures Correlates with Treatment Outcome in Major Depressive Disorder
Kozel, F. A., Rao, U., Lu, H., Nakonezny, P. A., Grannemann, B., McGregor, T., Croarkin, P. E., Mapes, K. S., Tamminga, C. A., & Trivedi, M. H. (2011) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2011.00007 · Cited by 61 · see citing works
Brain white-matter hyperintensities and treatment outcome in major depressive disorder
Iosifescu, D. V., Renshaw, P. F., Lyoo, I. K., Lee, H. K., Perlis, R. H., Papakostas, G. I., Nierenberg, A. A., & D, M. F. M. (2006) · The British Journal of Psychiatry · doi:10.1192/bjp.188.2.180 · Cited by 60 · see citing works
Clinical, behavioral, and neural measures of reward processing correlate with escitalopram response in depression: a Canadian Biomarker Integration Network in Depression (CAN-BIND-1) Report
Dunlop, K., Rizvi, S. J., Kennedy, S. H., Hassel, S., Strother, S. C., Harris, J. K., Zamyadi, M., Arnott, S. R., Davis, A. D., Mansouri, F., et al. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0688-x · Cited by 60 · see citing works
One-Year Change in Anterior Cingulate Cortex White Matter Microstructure: Relationship With Late-Life Depression Outcomes
Taylor, W. D., MacFall, J. R., Boyd, B. D., Payne, M. E., Sheline, Y. I., Krishnan, R. R., & Doraiswamy, P. M. (2010) · American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e3181e70cec · Cited by 59 · see citing works
Self-citation
Prediction of antidepressant treatment response from gray matter volume across diagnostic categories
Sämann, P. G., Höhn, D., Chechko, N., Kloiber, S., Lucae, S., Ising, M., Holsboer, F., & Czisch, M. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2013.07.004 · Cited by 55 · see citing works
MRI signal hyperintensities and failure to remit following antidepressant treatment
Sneed, J. R., Culang‐Reinlieb, M. E., Brickman, A. M., Gunning‐Dixon, F. M., Johnert, L. C., Garcon, E., & Roose, S. P. (2011) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.06.052 · Cited by 54 · see citing works
Optimizing Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM): Study Design and Treatment Characteristics of the First 396 Participants Randomized
Cristancho, P., Lenard, E., Lenze, E. J., Miller, J. P., Brown, P. J., Roose, S. P., Montes-Garcia, C., Blumberger, D. M., Mulsant, B. H., Lavretsky, H., et al. (2019) · The American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2019.04.005 · Cited by 54 · see citing works
Magnetic Resonance Imaging Predictors of Treatment Response in Late-Life Depression
Aizenstein, H. J., Khalaf, A. M., Walker, S. E., & Andreescu, C. (2013) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/0891988713516541 · Cited by 52 · see citing works
Self-citation
Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study
Meyer, B. M., Rabl, U., Huemer, J., Bartova, L., Kalcher, K., Provenzano, J., Brandner, C., Sezen, P., Kasper, S., Schatzberg, A. F., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0395-8 · Cited by 52 · see citing works
White Matter Hyperintensity Accumulation During Treatment of Late-Life Depression
Khalaf, A. M., Edelman, K. L., Tudorascu, D., Andreescu, C., Reynolds, C. F., & Aizenstein, H. J. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.158 · Cited by 50 · see citing works
Self-citation
Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex
Tian, S., Sun, Y., Shao, J., Zhang, S., Mo, Z., Liu, X., Wang, Q., Wang, L., Zhao, P., Chattun, M. R., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24872 · Cited by 49 · see citing works
Prediction of Selective Serotonin Reuptake Inhibitor Response Using Diffusion-Weighted MRI
DeLorenzo, C., Delaparte, L., Thapa-Chhetry, B., Miller, J. M., Mann, J. J., & Parsey, R. V. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00005 · Cited by 47 · see citing works
Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder
Martens, M., Filippini, N., Harmer, C. J., & Godlewska, B. R. (2021) · Psychopharmacology · doi:10.1007/s00213-021-05915-7 · Cited by 47 · see citing works
Limbic white matter microstructure plasticity reflects recovery from depression
Bracht, T., Jones, D. K., Müller, T., Wiest, R., & Walther, S. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.08.031 · Cited by 46 · see citing works
White Matter Indices of Medication Response in Major Depression: A Diffusion Tensor Imaging Study
Davis, A. D., Hassel, S., Arnott, S. R., Harris, J. K., Lam, R. W. M., Milev, R. V., Rotzinger, S., Zamyadi, M., Frey, B. N., Minuzzi, L., et al. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.05.016 · Cited by 46 · see citing works
Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals
Greenberg, T., Fournier, J. C. J., Stiffler, R. S., Chase, H. W., Almeida, J., Aslam, H. A., Deckersbach, T., Cooper, C. M., Toups, M. S. P., Carmody, T., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0490-5 · Cited by 46 · see citing works
MRI subcortical hyperintensities in old and very old depressed outpatients
Salloway, S., Correia, S., Boyle, P. A., Malloy, P., Schneider, L. S. S., Lavretsky, H., Sackheim, H. A., Roose, S. P., & Krishnan, K. (2002) · Journal of the Neurological Sciences · doi:10.1016/s0022-510x(02)00296-4 · Cited by 45 · see citing works
Neural response to emotional stimuli associated with successful antidepressant treatment and behavioral activation
Rizvi, S. J., Salomons, T. V., Konarski, J. Z., Downar, J., Giacobbe, P., McIntyre, R. S., & Kennedy, S. H. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.06.050 · Cited by 45 · see citing works
Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder
Cui, J., Wang, Y., Liu, R., Chen, X., Zhang, Z., Feng, Y., Zhou, J., Zhou, Y., & Wang, G. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01754-4 · Cited by 42 · see citing works
Brain-Volume Increase With Sustained Remission in Patients With Treatment-Resistant Unipolar Depression
Phillips, J. L., Batten, L. A., Aldosary, F., Tremblay, P., & Blier, P. (2012) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.11m06865 · Cited by 39 · see citing works
White Matter Hyperintensities on Magnetic Resonance Imaging: What Is a Clinician to Do?
Merino, J. G. (2019) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2019.01.016 · Cited by 39 · see citing works
Prediction of Nonremission to Antidepressant Therapy Using Diffusion Tensor Imaging
Grieve, S. M., Korgaonkar, M. S., Gordon, E., Williams, L. M., & Rush, A. J. (2016) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.14m09577 · Cited by 37 · see citing works
Predicting treatment effects in unipolar depression: A meta-review
Gillett, G., Tomlinson, A., Efthimiou, O., & Cipriani, A. (2020) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2020.107557 · Cited by 33 · see citing works
Prediction of Individual Differences from Neuroimaging Data
Calhoun, V. D., Lawrie, S. M., Mourão-Miranda, J., & Stephan, K. E. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.012 · Cited by 33 · see citing works
Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression
Karim, H. T., Wang, M. B., Andreescu, C., Tudorascu, D., Butters, M. A., Karp, J. F., Reynolds, C. F., & Aizenstein, H. J. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.06.006 · Cited by 32 · see citing works
Self-citation
Duloxetine effects on striatal resting‐state functional connectivity in patients with major depressive disorder
Wang, L., An, J., Gao, H., Zhang, P., Chen, C., Li, K., Mitchell, P. B., & Si, T. (2019) · Human Brain Mapping · doi:10.1002/hbm.24601 · Cited by 32 · see citing works
Resting-state brain activation correlates with short-time antidepressant treatment outcome in drug-naïve patients with major depressive disorder
Wang, L., Kuang, W., Xu, J., Lei, D., & Yang, Y. (2014) · Journal of International Medical Research · doi:10.1177/0300060514533524 · Cited by 31 · see citing works
Major Depressive Disorder
Winer, E. S., Salem, T., Bartoszek, G., & Snodgrass, M. (2015) · The Encyclopedia of Clinical Psychology · doi:10.1002/9781118625392.wbecp433 · Cited by 31 · see citing works
Hippocampal activation during associative encoding of word pairs and its relation to symptomatic improvement in depression: A functional and volumetric MRI study
Toki, S., Okamoto, Y., Onoda, K., Matsumoto, T., Yoshimura, S., Kunisato, Y., Okada, G., Shishida, K., Kobayakawa, M., Fukumoto, T., et al. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.07.021 · Cited by 29 · see citing works
Functional impairment‐based segmentation of anterior cingulate cortex in depression and its relationship with treatment effects
Zhang, Y., Shao, J., Wang, X., Chen, Z., Liu, H., Pei, C., Zhang, S., Yao, Z., & Lü, Q. (2021) · Human Brain Mapping · doi:10.1002/hbm.25537 · Cited by 27 · see citing works
Functional stability predicts depressive and cognitive improvement in major depressive disorder: A longitudinal functional MRI study
Li, X., Zhang, Y., Meng, C., Zhang, C., Zhao, W., Zhu, D., & Zhu, J. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110396 · Cited by 27 · see citing works
A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants
Nemati, S., Akiki, T. J., Roscoe, J., Ju, Y., Averill, C. L., Fouda, S., Dutta, A., McKie, S., Krystal, J. H., Deakin, J. F. W., et al. (2019) · iScience · doi:10.1016/j.isci.2019.100800 · Cited by 26 · see citing works
Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder
Xiao, H., Yuan, M., Li, H., Li, S., Du, Y., Wang, M., Zhu, H., Zhang, W., Qiu, C., & Huang, X. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.05.013 · Cited by 25 · see citing works
Global topology alteration of the brain functional network affects the 8-week antidepressant response in major depressive disorder
Zhang, Y., Liu, X., Hou, Z., Yin, Y., Xie, C., Zhang, H., Zhang, H., Kong, Y., Gao, S., Zhang, Z., et al. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.07.078 · Cited by 24 · see citing works
Prognostic value of imbalanced interhemispheric functional coordination in early therapeutic efficacy in major depressive disorder
Hou, Z., Song, X., Jiang, W., Yue, Y., Yin, Y., Zhang, Y., Liu, Y., & Yuan, Y. (2016) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2016.07.011 · Cited by 23 · see citing works
SSRI Treatment Response Prediction in Depression Based on Brain Activation by Emotional Stimuli
Preuss, A., Bolliger, B., Schicho, W., Hättenschwiler, J., Seifritz, E., Brühl, A. B., & Herwig, U. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.538393 · Cited by 22 · see citing works
Response to SSRI intervention and amygdala activity during self-referential processing in major depressive disorder
Young, K. D., Friedman, E. S., Collier, A. B., Berman, S., Feldmiller, J., Haggerty, A. E., Thase, M. E., & Siegle, G. J. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102388 · Cited by 22 · see citing works
Increased ventromedial prefrontal cortex activity and connectivity predict poor sertraline treatment outcome in late‐life depression
Emam, H., Steffens, D. C., Pearlson, G. D., & Wang, L. (2019) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5079 · Cited by 21 · see citing works
Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder
Fan, S., Nemati, S., Akiki, T. J., Roscoe, J., Averill, C. L., Fouda, S., Averill, L. A., & Abdallah, C. G. (2020) · Chronic Stress · doi:10.1177/2470547020984726 · Cited by 21 · see citing works
NATURALISTIC COURSE OF MAJOR DEPRESSIVE DISORDER PREDICTED BY CLINICAL AND STRUCTURAL NEUROIMAGING DATA: A 5-YEAR FOLLOW-UP: Research Article: Prediction of Long-Term Depressive Outcomes
Serra-Blasco, M., de Diego-Adeliño, J., Vives-Gilabert, Y., Trujols, J., Puigdemont, D., Carceller-Sindreu, M., Pérez, V., Álvarez, E., & Portella, M. J. (2016) · Depression and Anxiety · doi:10.1002/da.22522 · Cited by 21 · see citing works
An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report
Suh, J. S., Minuzzi, L., Raamana, P. R., Davis, A. D., Hall, G. B., Harris, J. K., Hassel, S., Zamyadi, M., Arnott, S. R., Alders, G. L., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102178 · Cited by 19 · see citing works
Baseline Functional Connectivity in Resting State Networks Associated with Depression and Remission Status after 16 Weeks of Pharmacotherapy: A CAN-BIND Report
van der Wijk, G., Harris, J. K., Hassel, S., Davis, A. D., Zamyadi, M., Arnott, S. R., Milev, R. V., Lam, R. W. M., Frey, B. N., Hall, G. B., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab286 · Cited by 19 · see citing works
Reward processing in major depressive disorder and prediction of treatment response – Neuropharm study
Brandt, I. M., Köhler‐Forsberg, K., Ganz, M., Ozenne, B., Jørgensen, M. B., Poulsen, A. S., Knudsen, G. M., Frøkjær, V. G., & Fisher, P. M. (2021) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.12.010 · Cited by 18 · see citing works
Cortical thickness predicts remission of depression with antidepressants in patients with late-life depression and cognitive impairment
Motter, J. N., Lee, S., Sneed, J. R., Doraiswamy, P. M., Pelton, G. H., Petrella, J. R., & Devanand, D. P. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.08.062 · Cited by 18 · see citing works
Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge
Klöbl, M., Gryglewski, G., Rischka, L., Godbersen, G. M., Unterholzner, J., Reed, M. B., Michenthaler, P., Vanicek, T., Pjrek, E., Hahn, A., et al. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.554186 · Cited by 17 · see citing works
Networks of worry—towards a connectivity-based signature of late-life worry using higher criticism
Gerlach, A., Karim, H. T., Kazan, J., Aizenstein, H. J., Krafty, R. T., & Andreescu, C. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01648-5 · Cited by 17 · see citing works
Self-citation
Examining raphe-amygdala structural connectivity as a biological predictor of SSRI response
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Self-citation
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Victoria, L. W., Alexopoulos, G. S., Ilieva, I. P., Stein, A. T., Hoptman, M. J., Chowdhury, N., Respino, M., Morimoto, S. S., Kanellopoulos, D., Avari, J., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.09.013 · Cited by 14 · see citing works
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Vieira, R., Coelho, A., Reis, J. S., Portugal‐Nunes, C., Magalhães, R. J. D. S., Ferreira, S., Moreira, P. S., Sousa, N., & Bessa, J. M. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.693109 · Cited by 14 · see citing works
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Self-citation
Early post‐treatment blood oxygenation level‐dependent responses to emotion processing associated with clinical response to pharmacological treatment in major depressive disorder
Williams, R. J., Brown, E. C., Clark, D., Pike, G. B., & Ramasubbu, R. (2021) · Brain and Behavior · doi:10.1002/brb3.2287 · Cited by 9 · see citing works
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Krause, B., Siddarth, P., Milillo, M. M., Vlasova, R., Ercoli, L. M., Narr, K. L., & Lavretsky, H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.548904 · Cited by 6 · see citing works

Flagged first, then by citations

1 of the 162 references couldn’t be matched to a known paper.

Research works printed but not linked: 9

Eisenberg Center at Oregon Health & Science University. Choosing Antidepressants for Adults: Clinician’s Guide. in Comparative Effectiveness Review Summary Guides for Clinicians (Agency for Healthcare Research and Quality (US), 2007). 21938806
Aizenstein H.J. Khalef A. Walker S.E. Andreescu C. MRI Predictors of Treatment Response in Late-Life Depression J. Geriatr. Psychiatry Neurol. 27 2014 24 32 24381231 PMC4103612
Practice guideline for the treatment of patients with major depressive disorder (revision). American Psychiatric Association. Am. J. Psychiatry157, 1–45 (2000). 10767867
Treatment outcomes and neural mechanisms DeRubeis, R. J., Siegle, G. J. & Hollon, S. D. Cognitive therapy vs. medications for depression Nat. Rev. Neurosci. 9 2008 788 796 18784657 PMC2748674
Frodl T. Effect of hippocampal and amygdala volumes on clinical outcomes in major depression: a 3-year prospective magnetic resonance imaging study J. Psychiatry Neurosci. JPN 33 2008 423 430 18787661 PMC2527720
Handbook of emotion regulation, 2nd ed. xviii, 669 (The Guilford Press, 2014).
Serra-Blasco M. NATURALISTIC COURSE OF MAJOR DEPRESSIVE DISORDER PREDICTED BY CLINICAL AND STRUCTURAL NEUROIMAGING DATA: A 5-YEAR FOLLOW-UP Depress. Anxiety 33 2016 1055 1064 27159902
Bennett, C. M., Baird, A. A., Miller, M. B. & L, G. Journal of Serendipitous and Unexpected Results Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument For Proper Multiple Comparisons Correction.
Ruhé H.G. Booij J. Veltman D.J. Michel M.C. Schene A.H. Successful Pharmacologic Treatment of Major Depressive Disorder Attenuates Amygdala Activation to Negative Facial Expressions: A Functional Magnetic Resonance Imaging Study J. Clin. Psychiatry 72 2011 21903032

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Low-dose interleukin 2 antidepressant potentiation in unipolar and bipolar depression: Safety, efficacy, and immunological biomarkers
Poletti, S., Zanardi, R., Mandelli, A., Aggio, V., Finardi, A., Lorenzi, C., Borsellino, G., Carminati, M., Manfredi, E., Tomasi, E., et al. (2024) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2024.02.019 · Cited by 50 · see citing works
Treatment-Resistant Late-Life Depression
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Self-citation
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Self-citation
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Self-citation
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Authors: Gerlach, A. R., Karim, H. T., Peciña, M., Ajilore, O., Taylor, W. D., Butters, M. A., & Andreescu, C.
Affiliations: University of Pittsburgh (US)
University of Illinois Chicago (US)
VA Tennessee Valley Healthcare System (US)
Corresponding author: Andreescu, Carmen
ORCID: Gerlach, Andrew R.
Karim, Helmet T.
Peciña, Marta
Ajilore, Olusola
Taylor, Warren D.
Butters, Meryl A.
Andreescu, Carmen
Venue: NeuroImage: Clinical
Publisher: Elsevier BV
Published: 2022
Volume: 36
Pages: 103157
ISSN: 2213-1582 (ISSN-L)
DOI: 10.1016/j.nicl.2022.103157
PMC: PMC9420953
OpenAlex: W4292342431
Cited by: 57
References: 162
Citations by year: 2026: 10 · 2025: 18 · 2024: 19 · 2023: 10
FWCI: 1.69
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 226 d)
Locations: NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Institute of Mental Health (K01MH122741, R01 MH076079, R01 MH102246, R01 MH108509, R01 MH121619, RO1 MH121620, T32 MH019986)
Topics: Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords: major depressive disorder, antidepressant response, treatment outcome prediction, brain MRI biomarkers, hippocampal volume, white matter hyperintensities, white matter integrity, fractional anisotropy, cingulum, anterior cingulate cortex, default mode network, frontoparietal network, resting-state fMRI, task-based fMRI
MeSH terms: Brain, Brain Mapping, Depressive Disorder, Major, Major Depressive Disorder, Humans, Magnetic Resonance Imaging, White Matter

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MRI predictors of pharmacotherapy response in major depressive disorder

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