The role of machine learning in neuroimaging for drug discovery and development

Psychopharmacology

Volumn 232, Issue 21-22, 2015, Pages 4179-4189

  Doyle, Orla M ^a   Mehta, Mitul A ^a   Brammer, Michael J ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Drug discovery; Experimental medicine models; Machine learning; Neuroimaging; Personalised medicine; Probabilistic models; Stratification

Indexed keywords

ANTIDEPRESSANT AGENT; CLOZAPINE; DONEPEZIL; KETAMINE; LAMOTRIGINE; OXYTOCIN; SCOPOLAMINE;

ACCURACY; AGORAPHOBIA; BLOOD FLOW; BOLD SIGNAL; BRAIN BLOOD FLOW; CHOLINERGIC TRANSMISSION; DRUG DEVELOPMENT; DRUG RESPONSE; ELECTROENCEPHALOGRAM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; INFORMATION PROCESSING; INFORMATION RETRIEVAL; KERNEL METHOD; MACHINE LEARNING; MENTAL DISEASE; NEUROIMAGING; PANIC; PERFUSION WEIGHTED IMAGING; PERSONALIZED MEDICINE; PREDICTION; PRIORITY JOURNAL; PROBABILITY; REPRODUCIBILITY; REVIEW; SCHIZOPHRENIA; SOCIAL BEHAVIOR; SUPPORT VECTOR MACHINE; VALIDITY; BRAIN; DRUG EFFECTS; HUMAN; PHYSIOLOGY; PROCEDURES;

BRAIN; DRUG DISCOVERY; HUMANS; MACHINE LEARNING; NEUROIMAGING;

EID: 84952699504     PISSN: 00333158     EISSN: 14322072     Source Type: Journal    
DOI: 10.1007/s00213-015-3968-0     Document Type: Review

References (57)

1. Borsook D, Becerra L, Hargreaves R (2006) A role for fMRI in optimizing CNS drug development. Nat Rev Drug Discov 5:411-424
2. Borsook D, Beccera L, Bullmore E, Hargreaves R (2010) Imaging in CNS drug discovery and development. Springer, New York
3. Borsook D, Becerra L, Fava M (2013) Use of functional imaging across clinical phases in CNS drug development. Transl Psychiat 3
4. Bruns A, Kunnecke B, Risterucci C, Moreau JL, von Kienlin M (2009) Validation of cerebral blood perfusion imaging as a modality for quantitative pharmacological MRI in rats. Magnetic Reson Med: Off J Soc Magn Reson Med/ Soc Magn Reson Med 61:1451-8
5. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186-98
6. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121-167
7. Bymaster FP, Heath I, Hendrix JC, Shannon HE (1993) Comparative behavioral and neurochemical activities of cholinergic antagonists in rats. J Pharmacol Exp Ther 267:16-24
8. Cherry S (1996) Singular value decomposition analysis and canonical correlation analysis. J Clim 9:2003-2009
9. Cho W, Maruff P, Connell J, Gargano C, Calder N, Doran S, Fox-Bosetti S, Hassan A, Renger J, Herman G, Lines C, Verma A (2011) Additive effects of a cholinesterase inhibitor and a histamine inverse agonist on scopolamine deficits in humans. Psychopharmacology 218:513-24
10. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273-297
11. Costafreda SG, Chu C, Ashburner J, Fu CH (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 4:e6353
12. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, Habert MO, Chupin M, Benali H, Colliot O (2011) Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56:766-81
13. De Simoni S, Schwarz AJ, O'Daly OD, Stephenson S, Zelaya FO, Williams SCR, Mehta MA (2013) Test-retest reliability of the BOLD pharmacological MRI response to ketamine in healthy volunteers. NeuroImage 64:75-90
14. Di Santo SG, Prinelli F, Adorni F, Caltagirone C, Musicco M (2013) A meta-analysis of the efficacy of donepezil, rivastigmine, galantamine, and memantine in relation to severity of Alzheimer's disease. J Alzheimers Dis 35:349-61
15. Dodds CM, Muller U, Clark L, van Loon A, Cools R, Robbins TW (2008) Methylphenidate has differential effects on blood oxygenation level-dependent signal related to cognitive subprocesses of reversal learning. J Neurosci: Off J Soc Neurosci 28:5976-82
16. Donaldson ZR, Young LJ (2008) Oxytocin, vasopressin, and the neurogenetics of sociality. Science 322:900-4
17. Doyle OM, Mehta MA, Brammer MJ, Schwartz AJ, De Simoni S, Marquand AF (2012) Data-driven modeling of BOLD drug response curves using Gaussian process learning. Springer Lecture Notes in Artificial Intelligence: 7263: In Press.
18. Doyle OM, Ashburner J, Zelaya FO, Williams SC, Mehta MA, Marquand AF (2013a) Multivariate decoding of brain images using ordinal regression. NeuroImage 81C:347-357
19. Doyle OM, De Simoni S, Schwarz AJ, Brittain C, O'Daly OG, Williams SCR, Mehta MA (2013) Quantifying the attenuation of the ketamine phMRI response in humans: a validation using antipsychotic and glutamatergic agents. J Pharmacol Exp Ther (In Press)
20. Doyle OM, Tsaneva-Atansaova K, Harte J, Tiffin PA, Tino P, Diaz-Zuccarini V (2013c) Bridging paradigms: hybrid mechanistic-discriminative predictive models. IEEE Trans Bio-Med Eng 60:735-42
21. Doyle OM, Westman E, Marquand AF, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Lovestone S, Williams SC, Simmons A (2014) Predicting progression of Alzheimer's disease using ordinal regression. PLoS One 9:e105542
22. Duff EP, Vennart W, Wise RG, Howard MA, Harris RE, Lee M, Wartolowska K, Wanigasekera V, Wilson FJ, Whitlock M, Tracey I, Woolrich MW, Smith SM (2015) Learning to identify CNS drug action and efficacy using multistudy fMRI data. Sci Trans Med 7:274ra16
23. Figueroa RL, Zeng-Treitler Q, Kandula S, Ngo LH (2012) Predicting sample size required for classification performance. BMC Med Inform Decis Making 12:8
24. Fu CH, Steiner H, Costafreda SG (2013) Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 52:75-83
25. Gong Q, Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, Huang X, McGuire P, Mechelli A (2011) Prognostic prediction of therapeutic response in depression using high-field MR imaging. NeuroImage 55:1497-503
26. Guo Y, Graber A, McBurney RN, Balasubramanian R (2010) Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms. BMC Bioinforma 11:447
27. Hahn T, Kircher T, Straube B, Wittchen HU, Konrad C, Strohle A, Wittmann A, Pfleiderer B, Reif A, Arolt V, Lueken U (2015) Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiatry 72:68-74
28. Haufe S, Meinecke F, Gorgen K, Dahne S, Haynes JD, Blankertz B, Biessmann F (2014) On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 87:96-110
29. Honey G, Bullmore E (2004) Human pharmacological MRI. Trends Pharmacol Sci 25:366-74
30. Insel TR (2012) Next-generation treatments for mental disorders. Science translational medicine 4
31. Insel TR (2014) The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. Am J Psychiatry 171:395-7
32. Joules R, Doyle OM, Schwarz AJ, O'Daly OG, Brammer MJ, Williams SCR, Mehta MA (2015) Ketamine induces a robust whole-brain connectivity pattern that can be differentially modulated by drugs of different mechanism and clinical profile. In submission
33. 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
34. Kendler KS, Eaves LJ, Walters EE, Neale MC, Heath AC, Kessler RC (1996) The identification and validation of distinct depressive syndromes in a population-based sample of female twins. Arch Gen Psychiatry 53:391-399
35. Khodayari-Rostamabad A, Hasey GM, Maccrimmon DJ, Reilly JP, de Bruin H (2010) A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clin Neurophysiol 121:1998-2006
36. Kriegeskorte N, Lindquist MA, Nichols TE, Poldrack RA, Vul E (2010) Everything you never wanted to know about circular analysis, but were afraid to ask. J Cereb Blood Flow Metab: Off J Int Soc Cereb Blood Flow Metab 30:1551-7
37. Krystal JH, Karper LP, Seibyl JP, Freeman GK, Delaney R, Bremner JD, Heninger GR, Bowers MB Jr, Charney DS (1994) Subanesthetic effects of the noncompetitive NMDA antagonist, ketamine, in humans. Psychotomimetic, perceptual, cognitive, and neuroendocrine responses. Arch Gen Psychiatry 51:199-214
38. Lavecchia A (2014) Machine-learning approaches in drug discovery: methods and applications. Drug discovery today
39. Lenz RA, Baker JD, Locke C, Rueter LE, Mohler EG, Wesnes K, Abi-Saab W, Saltarelli MD (2012) The scopolamine model as a pharmacodynamic marker in early drug development. Psychopharmacology 220:97-107
40. Liao Q, Wang J, Webster Y, Watson IA (2009) GPU accelerated support vector machines for mining high-throughput screening data. J Chem Inf Model 49:2718-25
41. Mak IWY, Evaniew N, Ghert M (2014) Lost in translation: animal models and clinical trials in cancer treatment. Am J Transl Res 6:114-118
42. Marquand AF, O'Daly OG, De Simoni S, Alsop DC, Maguire RP, Williams SC, Zelaya FO, Mehta MA (2012) Dissociable effects of methylphenidate, atomoxetine and placebo on regional cerebral blood flow in healthy volunteers at rest: a multi-class pattern recognition approach. NeuroImage 60:1015-24
43. Medhi B, Misra S, Avti PK, Kumar P, Kumar H, Singh B (2014) Role of neuroimaging in drug development. Rev Neurosci 25:663-673
44. Mehta M, O'Daly O (2011) Pharmacological Application of fMRI. In: Modo M, Bulte JWM (eds) Magnetic resonance neuroimaging (methods in molecular biology). Humana Press, pp 551-565
45. Oquendo MA, Baca-Garcia E, Artes-Rodriguez A, Perez-Cruz F, Galfalvy HC, Blasco-Fontecilla H, Madigan D, Duan N (2012) Machine learning and data mining: strategies for hypothesis generation. Mol Psychiatry 17:956-9
46. Paloyelis Y, Doyle OM, Zelaya FO, Maltezos S, Williams SC, Fotopoulou A, Howard MA (2014) A spatiotemporal profile of in vivo cerebral blood flow changes following intranasal oxytocin in humans. Biol Psychiatry
47. Pauls AM, O'Daly OG, Rubia K, Riedel WJ, Williams SC, Mehta MA (2012) Methylphenidate effects on prefrontal functioning during attentional-capture and response inhibition. Biol Psychiatry 72:142-9
48. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge, Mass
49. Schwarz AJ, Becerra L, Upadhyay J, Anderson J, Baumgartner R, Coimbra A, Evelhoch J, Hargreaves R, Robertson B, Iyengar S, Tauscher J, Bleakman D, Borsook D (2011a) A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #1: processes and requirements. Drug Discov Today 16:583-93
50. Schwarz AJ, Becerra L, Upadhyay J, Anderson J, Baumgartner R, Coimbra A, Evelhoch J, Hargreaves R, Robertson B, Iyengar S, Tauscher J, Bleakman D, Borsook D (2011b) A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #2: protocol optimization and best practices. Drug Discov Today 16:671-82
51. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge
52. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, Woolrich MW (2011) Network modelling methods for FMRI. NeuroImage 54:875-91
53. Sullivan PF, Kessler RC, Kendler KS (1998) Latent class analysis of lifetime depressive symptoms in the national comorbidity survey. Am J Psychiatry 155:1398-406
54. Sun LA, Ji SW, Yu SP, Ye JP (2009) On the equivalence between canonical correlation analysis and orthonormalized partial least squares. 21st International Joint Conference on Artificial Intelligence (Ijcai-09), Proceedings: 1230-1235.
55. Wang DJ, Chen Y, Fernandez-Seara MA, Detre JA (2011) Potentials and challenges for arterial spin labeling in pharmacological magnetic resonance imaging. J Pharmacol Exp Ther 337:359-66
56. Wise RG, Preston C (2010) What is the value of human FMRI in CNS drug development? Drug Discov Today 15:973-980
57. Wong DF, Tauscher J, Grunder G (2009) The role of imaging in proof of concept for CNS drug discovery and development. Neuropsychopharmacol 34:187-203