Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach

References

• Alonso J, Petukhova M, Vilagut G, Chatterji S, Heeringa S, Üstün TB, Alhamzawi AO, Viana MC, Angermeyer M, Bromet E, et al. 2011. Days out of role due to common physical and mental conditions: results from the WHO world mental health surveys. Mol Psychiatry. 16(12):1234–1246. doi: 10.1038/mp.2010.101.
   PubMed Web of Science ®Google Scholar
• Alves PN, Foulon C, Karolis V, Bzdok D, Margulies DS, Volle E, De Schotten MT. 2019. An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings. Commun Biol. 2(1):370. doi: 10.1038/s42003-019-0611-3.
   PubMedGoogle Scholar
• Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. 2010. Functional-anatomic fractionation of the brain’s default network. Neuron. 65(4):550–562. doi: 10.1016/j.neuron.2010.02.005.
   PubMed Web of Science ®Google Scholar
• Bari AA, Mikell CB, Abosch A, Ben-Haim S, Buchanan RJ, Burton AW, Carcieri S, Cosgrove GR, D'Haese PF, Daskalakis ZJ, et al. 2018. Charting the road forward in psychiatric neurosurgery: proceedings of the 2016 American society for stereotactic and functional neurosurgery workshop on neuromodulation for psychiatric disorders. J Neurol Neurosurg Psychiatry. 89(8):886–896. doi: 10.1136/jnnp-2017-317082.
   PubMed Web of Science ®Google Scholar
• Broadway JM, Holtzheimer PE, Hilimire MR, Parks NA, Devylder JE, Mayberg HS, Corballis PM. 2012. Frontal theta cordance predicts 6-month antidepressant response to subcallosal cingulate deep brain stimulation for treatment-resistant depression: a pilot study. Neuropsychopharmacology. 37(7):1764–1772. doi: 10.1038/npp.2012.23.
   PubMed Web of Science ®Google Scholar
• Brown EC, Clark DL, Forkert ND, Molnar CP, Kiss ZHT, Ramasubbu R. 2020. Metabolic activity in subcallosal cingulate predicts response to deep brain stimulation for depression. Neuropsychopharmacology. 45(10):1681–1688. doi: 10.1038/s41386-020-0745-5.
   PubMed Web of Science ®Google Scholar
• Buckner RL, Andrews-Hanna JR, Schacter DL. 2008. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 1124(1):1–38. doi: 10.1196/annals.1440.011.
   PubMed Web of Science ®Google Scholar
• Cervenka S, Frick A, Bodén R, Lubberink M. 2022. Application of positron emission tomography in psychiatry-methodological developments and future directions. Transl Psychiatry. 12(1):248. doi: 10.1038/s41398-022-01990-2.
   PubMed Web of Science ®Google Scholar
• Cha YH, Jog MA, Kim YC, Chakrapani S, Kraman SM, Wang DJ. 2013. Regional correlation between resting state FDG PET and pCASL perfusion MRI. J Cereb Blood Flow Metab. 33(12):1909–1914. doi: 10.1038/jcbfm.2013.147.
   PubMed Web of Science ®Google Scholar
• Clark DL, MacMaster FP, Brown EC, Kiss ZHT, Ramasubbu R. 2020. Rostral anterior cingulate glutamate predicts response to subcallosal deep brain stimulation for resistant depression. J Affect Disord. 266:90–94. doi: 10.1016/j.jad.2020.01.058.
   PubMed Web of Science ®Google Scholar
• Conen S, Matthews JC, Patel NK, Anton-Rodriguez J, Talbot PS. 2018. Acute and chronic changes in brain activity with deep brain stimulation for refractory depression. J Psychopharmacol. 32(4):430–440. doi: 10.1177/0269881117742668.
   PubMed Web of Science ®Google Scholar
• Conway CR, Chibnall JT, Gangwani S, Mintun MA, Price JL, Hershey T, Giuffra LA, Bucholz RD, Christensen JJ, Sheline YI. 2012. Pretreatment cerebral metabolic activity correlates with antidepressant efficacy of vagus nerve stimulation in treatment-resistant major depression: a potential marker for response? J Affect Disord. 139(3):283–290. doi: 10.1016/j.jad.2012.02.007.
   PubMed Web of Science ®Google Scholar
• Cooper CM, Chin Fatt CR, Jha M, Fonzo GA, Grannemann BD, Carmody T, Ali A, Aslan S, Almeida JRC, Deckersbach T, et al. 2019. Cerebral blood perfusion predicts response to sertraline versus placebo for major depressive disorder in the EMBARC trial. EClinicalMedicine. 10:32–41. doi: 10.1016/j.eclinm.2019.04.007.
   PubMedGoogle Scholar
• De Crescenzo F, Ciliberto M, Menghini D, Treglia G, Ebmeier KP, Janiri L. 2017. Is F-18-FDG-PET suitable to predict clinical response to the treatment of geriatric depression? A systematic review of PET studies. Aging Ment Health. 21(9):889–894. doi: 10.1080/13607863.2016.1247413.
   PubMed Web of Science ®Google Scholar
• Dey L. 2016. Sentiment analysis of review datasets using naive Bayes and K-NN classifiers. arXiv preprint arXiv:1610.09982.
   Google Scholar
• Dougherty DD, Rezai AR, Carpenter LL, Howland RH, Bhati MT, O'Reardon JP, Eskandar EN, Baltuch GH, Machado AD, Kondziolka D, et al. 2015. A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression. Biol Psychiatry. 78(4):240–248. doi: 10.1016/j.biopsych.2014.11.023.
   PubMed Web of Science ®Google Scholar
• Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, et al. 2017. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 23(1):28–38. doi: 10.1038/nm.4246.
   PubMed Web of Science ®Google Scholar
• Elias GJB, Germann J, Boutet A, Pancholi A, Beyn ME, Bhatia K, Neudorfer C, Loh A, Rizvi SJ, Bhat V, et al. 2022. Structuro-functional surrogates of response to subcallosal cingulate deep brain stimulation for depression. Brain. 145(1):362–377. doi: 10.1093/brain/awab284.
   PubMed Web of Science ®Google Scholar
• Fransson P, Marrelec G. 2008. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis. Neuroimage. 42(3):1178–1184. doi: 10.1016/j.neuroimage.2008.05.059.
   PubMed Web of Science ®Google Scholar
• Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC. 2015. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 76(2):155–162. doi: 10.4088/JCP.14m09298.
   PubMed Web of Science ®Google Scholar
• Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, Reiss AL, Schatzberg AF. 2007. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 62(5):429–437. doi: 10.1016/j.biopsych.2006.09.020.
   PubMed Web of Science ®Google Scholar
• Guilloux JP, Bassi S, Ding Y, Walsh C, Turecki G, Tseng G, Cyranowski JM, Sibille E. 2015. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression. Neuropsychopharmacology. 40(3):701–710. doi: 10.1038/npp.2014.226.
   PubMed Web of Science ®Google Scholar
• Habeck C, Stern Y; Alzheimer’s Disease Neuroimaging Initiative. 2010. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease. Cell Biochem Biophys. 2010 Nov;58(2):53-67. doi: 10.1007/s12013-010-9093-0. PMID: 20658269; PMCID: PMC3001346
   Google Scholar
• Hamilton JP, Furman DJ, Chang C, Thomason ME, Dennis E, Gotlib IH. 2011. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol Psychiatry. 70(4):327–333. doi: 10.1016/j.biopsych.2011.02.003.
   PubMed Web of Science ®Google Scholar
• Hamilton M. 1960. A rating scale for depression. J Neurol Neurosurg Psychiatry. 23(1):56–62. doi: 10.1136/jnnp.23.1.56.
   PubMed Web of Science ®Google Scholar
• He X, Raichle ME, Yablonskiy DA. 2012. Transmembrane dynamics of water exchange in human brain. Magn Reson Med. 67(2):562–571. doi: 10.1002/mrm.23019.
   PubMed Web of Science ®Google Scholar
• Holte RC. 1993. Very simple classification rules perform well on most commonly used datasets. Mach Learn. 11(1):63–90. doi: 10.1023/A:1022631118932.
   Web of Science ®Google Scholar
• Holtzheimer PE, Husain MM, Lisanby SH, Taylor SF, Whitworth LA, McClintock S, Slavin KV, Berman J, McKhann GM, Patil PG, et al. 2017. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry. 4(11):839–849. doi: 10.1016/S2215-0366(17)30371-1.
   PubMed Web of Science ®Google Scholar
• Ishak WW, Balayan K, Bresee C, Greenberg JM, Fakhry H, Christensen S, Rapaport MH. 2013. A descriptive analysis of quality of life using patient-reported measures in major depressive disorder in a naturalistic outpatient setting. Qual Life Res. 22(3):585–596. doi: 10.1007/s11136-012-0187-6.
   PubMed Web of Science ®Google Scholar
• John GH, Langley P. 1995. Estimating continous distributions in Bayesian classifiers. In: Besnard P, Hanks S, editors. Eleventh conference on uncertainity in artificial intelligence. San Francisco: Morgan Kaufmann Publishers Inc; p. 338–345.
   Google Scholar
• Lee Y, Ragguett RM, Mansur RB, Boutilier JJ, Rosenblat JD, Trevizol A, Brietzke E, Lin K, Pan Z, Subramaniapillai M, et al. 2018. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review. J Affect Disord. 274:1211–1215. doi: 10.1016/j.jad.2018.08.073.
   Web of Science ®Google Scholar
• Lim KL, Jacobs P, Ohinmaa A, Schopflocher D, Dewa CS. 2008. A new population-based measure of the economic burden of mental illness in Canada. Chronic Dis Can. 28(3):92–98. doi: 10.24095/hpcdp.28.3.02.
   PubMed Web of Science ®Google Scholar
• Lo Vercio L, Amador K, Bannister JJ, Crites S, Gutierrez A, MacDonald ME, Moore J, Mouches P, Rajashekar D, Schimert S, et al. 2020. Supervised machine learning tools: a tutorial for clinicians. J Neural Eng. 17(6):062001. doi: 10.1088/1741-2552/abbff2.
   Web of Science ®Google Scholar
• MacEachern SJ, Forkert ND. 2021. Machine learning for precision medicine. Genome. 64(4):416–425. doi: 10.1139/gen-2020-0131.
   PubMed Web of Science ®Google Scholar
• Mayberg HS. 2003. Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. Br Med Bull. 65(1):193–207. doi: 10.1093/bmb/65.1.193.
   PubMedGoogle Scholar
• McGrath CL, Kelley ME, Holtzheimer PE, Dunlop BW, Craighead WE, Franco AR, Craddock RC, Mayberg HS. 2013. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry. 70(8):821–829. doi: 10.1001/jamapsychiatry.2013.143.
   PubMed Web of Science ®Google Scholar
• McInerney SJ, McNeely HE, Geraci J, Giacobbe P, Rizvi SJ, Ceniti AK, Cyriac A, Mayberg HS, Lozano AM, Kennedy SH. 2017. Neurocognitive predictors of response in treatment resistant depression to subcallosal cingulate gyrus deep brain stimulation. Front Hum Neurosci. 11:74. doi: 10.3389/fnhum.2017.00074.
   PubMed Web of Science ®Google Scholar
• Newberg AB, Wang J, Rao H, Swanson RL, Wintering N, Karp JS, Alavi A, Greenberg JH, Detre JA. 2005. Concurrent CBF and CMRGlc changes during human brain activation by combined fMRI-PET scanning. Neuroimage. 28(2):500–506. doi: 10.1016/j.neuroimage.2005.06.040.
   PubMed Web of Science ®Google Scholar
• Ojala M, Garriga GC. 2010. Permutation tests for studying classifier performance. J Mach Learn Res. 11:908–913. doi: 10.1109/ICDM.2009.108.
   Web of Science ®Google Scholar
• Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF, Aizenstein HJ. 2015. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry. 30(10):1056–1067. doi: 10.1002/gps.4262.
   PubMed Web of Science ®Google Scholar
• Quraan MA, Protzner AB, Daskalakis ZJ, Giacobbe P, Tang CW, Kennedy SH, Lozano AM, McAndrews MP. 2014. EEG power asymmetry and functional connectivity as a marker of treatment effectiveness in DBS surgery for depression. Neuropsychopharmacology. 39(5):1270–1281. doi: 10.1038/npp.2013.330.
   PubMed Web of Science ®Google Scholar
• Ramasubbu R, Clark DL, Golding S, Dobson KS, Mackie A, Haffenden A, Kiss ZH. 2020. Long versus short pulse width subcallosal cingulate stimulation for treatment-resistant depression: a randomised, double-blind, crossover trial. Lancet Psychiatry. 7(1):29–40. doi: 10.1016/S2215-0366(19)30415-8.
   PubMed Web of Science ®Google Scholar
• Ramasubbu R, Lang S, Kiss ZHT. 2018. Dosing of electrical parameters in deep brain stimulation (DBS) for intractable depression: a review of clinical studies. Front Psychiatry. 9:302. doi: 10.3389/fpsyt.2018.00302.
   PubMed Web of Science ®Google Scholar
• Riva-Posse P, Choi KS, Holtzheimer PE, Crowell AL, Garlow SJ, Rajendra JK, McIntyre CC, Gross RE, Mayberg HS. 2018. A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol Psychiatry. 23(4):843–849. doi: 10.1038/mp.2017.59.
   PubMed Web of Science ®Google Scholar
• Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, Niederehe G, Thase ME, Lavori PW, Lebowitz BD, et al. 2006. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. AJP. 163(11):1905–1917. doi: 10.1176/ajp.2006.163.11.1905.
   Web of Science ®Google Scholar
• Sankar T, Chakravarty MM, Jawa N, Li SX, Giacobbe P, Kennedy SH, Rizvi SJ, Mayberg HS, Hamani C, Lozano AM. 2020. Neuroanatomical predictors of response to subcallosal cingulate deep brain stimulation for treatment-resistant depression. J Psychiatry Neurosci. 45(1):45–54. doi: 10.1503/jpn.180207.
   PubMed Web of Science ®Google Scholar
• Scangos KW, Khambhati AN, Daly PM, Makhoul GS, Sugrue LP, Zamanian H, Liu TX, Rao VR, Sellers KK, Dawes HE, et al. 2021. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat Med. 27(10):1696–1700. doi: 10.1038/s41591-021-01480-w.
   PubMed Web of Science ®Google Scholar
• Schwartz WJ, Smith CB, Davidsen L, Savaki H, Sokoloff L, Mata M, Fink DJ, Gainer H. 1979. Metabolic mapping of functional-activity in the hypothalamo-neurohypophyseal system of the rat. Science. 205(4407):723–725. doi: 10.1126/science.462184.
   PubMed Web of Science ®Google Scholar
• Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, Mintun MA, Wang S, Coalson RS, Raichle ME. 2009. The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A. 106(6):1942–1947. doi: 10.1073/pnas.0812686106.
   PubMed Web of Science ®Google Scholar
• Smith KS, Tindell AJ, Aldridge JW, Berridge KC. 2009. Ventral pallidum roles in reward and motivation. Behav Brain Res. 196(2):155–167. doi: 10.1016/j.bbr.2008.09.038.
   PubMed Web of Science ®Google Scholar
• Sokoloff L, Reivich M, Kennedy C, Des Rosiers MH, Patlak CS, Pettigrew KD, Sakurada O, Shinohara M. 1977. The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem. 28(5):897–916. doi: 10.1111/j.1471-4159.1977.tb10649.x.
   PubMed Web of Science ®Google Scholar
• Sokoloff L. 1981. Localization of functional activity in the Central nervous system by measurement of glucose utilization with radioactive deoxyglucose. J Cereb Blood Flow Metab. 1(1):7–36. doi: 10.1038/jcbfm.1981.4.
   PubMed Web of Science ®Google Scholar
• Sui J, Adali T, Yu Q, Chen J, Calhoun VD. 2012. A review of multivariate methods for multimodal fusion of brain imaging data. J Neurosci Methods. 204(1):68–81. doi: 10.1016/j.jneumeth.2011.10.031.
   PubMed Web of Science ®Google Scholar
• Talati A, Van Dijk MT, Pan L, Hao X, Wang Z, Gameroff M, Dong Z, Kayser J, Shankman S, Wickramaratne PJ, et al. 2022. Putamen structure and function in familial risk for depression: a multimodal imaging study. Biol Psychiatry. 92(12):932–941. doi: 10.1016/j.biopsych.2022.06.035.
   PubMed Web of Science ®Google Scholar
• Winter N, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, Barkhau C, Thiel K, Flinkenflügel K, Winter A, et al. 2023. A systematic evaluation of machine learning-based biomarkers for major depressive disorder across modalities. medRxiv 2023.02.27.23286311.
   Google Scholar
• Wise T, Marwood L, Perkins AM, Herane-Vives A, Joules R, Lythgoe DJ, Luh WM, Williams SCR, Young AH, Cleare AJ, et al. 2017. Instability of default mode network connec­tivity in major depression: a two-sample confirmation study. Transl Psychiatry. 7(4):e1105–e1105. doi: 10.1038/tp.2017.40.
   PubMedGoogle Scholar
• Zamoscik V, Huffziger S, Ebner-Priemer U, Kuehner C, Kirsch P. 2014. Increased involvement of the parahippocampal gyri in a sad mood predicts future depressive symptoms. Soc Cogn Affect Neurosci. 9(12):2034–2040. doi: 10.1093/scan/nsu006.
   PubMed Web of Science ®Google Scholar
• Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D. 2012. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain. 135(Pt 5):1498–1507. doi: 10.1093/brain/aws059.
   PubMedGoogle Scholar