Neuroadaptive Bayesian Optimization and Hypothesis Testing

Trends in Cognitive Sciences

Volumn 21, Issue 3, 2017, Pages 155-167

  Lorenz, Romy ^a   Hampshire, Adam ^a   Leech, Robert ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

BAYESIAN OPTIMIZATION; COGNITIVE SCIENCE; EXPERIMENTAL CONDITIONS; HYPOTHESIS TESTING; REAL TIME DATA ANALYSIS; REPRODUCIBILITIES; RESEARCH QUESTIONS;

STATISTICAL TESTS;

ARTIFICIAL INTELLIGENCE; DATA ANALYSIS; EXPERIMENTAL DESIGN; EXPERIMENTAL MODEL; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MACHINE LEARNING; NEUROIMAGING; PSYCHOLOGY; REPRODUCIBILITY; ROBOTICS; SAMPLING; SCIENTIST; BAYES THEOREM; COGNITION; LEARNING; STATISTICAL MODEL;

BAYES THEOREM; COGNITION; GENERALIZATION (PSYCHOLOGY); HUMANS; MODELS, STATISTICAL; REPRODUCIBILITY OF RESULTS;

EID: 85013498131     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2017.01.006     Document Type: Review

References (101)

1. 1 Poldrack, R.A., Farah, M.J., Progress and challenges in probing the human brain. Nature 526 (2015), 371–379.
2. 2 Glasser, M.F., et al. A multi-modal parcellation of human cerebral cortex. Nature 536 (2016), 171–178.
3. 3 Huth, A.G., et al. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532 (2016), 453–458.
4. 4 Eklund, A., et al. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl. Acad. Sci. 113 (2016), 7900–7905.
5. 5 Ioannidis, J.P.A., Why most published research findings are false. PLoS Med., 2, 2005, e124.
6. 6 Ioannidis, J.P.A., et al. Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention. Trends Cogn. Sci. 18 (2014), 235–241.
7. 7 Vul, E., et al. Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4 (2009), 274–290.
8. 8 Head, M.L., et al. The extent and consequences of P-hacking in science. PLOS Biol., 13, 2015, e1002106.
9. 9 Szucs, D., Ioannidis, J.P., Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. bioRxiv., 2016, 10.1101/071530 Published online August 25, 2016.
10. 10 Collaboration, O.S., Estimating the reproducibility of psychological science. Science, 349, 2015, aac4716.
11. 11 John, L.K., et al. Measuring the prevalence of questionable research practices with incentives for truth telling. Psychol. Sci. 23 (2012), 524–532.
12. 12 Chambers, C.D., Registered reports: a new publishing initiative at Cortex. Cortex 49 (2013), 609–610.
13. 13 Nosek, B.A., et al. Promoting an open research culture. Science 348 (2015), 1422–1425.
14. 14 Gorgolewski, K.J., Poldrack, R.A., A practical guide for improving transparency and reproducibility in neuroimaging research. PLOS Biol, 14, 2016, e1002506.
15. 15 Westfall, J., et al. Fixing the stimulus-as-fixed-effect fallacy in task fMRI. bioRxiv., 2016, 10.1101/077131 Published online October 12, 2016.
16. 16 Hampshire, A., Sharp, D.J., Contrasting network and modular perspectives on inhibitory control. Trends Cogn. Sci. 19 (2015), 445–452.
17. 17 Shibata, K., et al. Differential activation patterns in the same brain region led to opposite emotional states. PLOS Biol, 14, 2016, e1002546.
18. 18 Wager, T.D., et al. Pain in the ACC?. Proc. Natl. Acad. Sci. 113 (2016), E2474–E2475.
19. 19 Erika-Florence, M., A functional network perspective on response inhibition and attentional control. Nat. Commun., 5, 2014, 4073.
20. 20 Leech, R., Sharp, D.J., The role of the posterior cingulate cortex in cognition and disease. Brain J. Neurol. 137 (2014), 12–32.
21. 21 Sulzer, J., et al. Real-time fMRI neurofeedback: progress and challenges. NeuroImage 76 (2013), 386–399.
22. 22 Shahriari, B., et al. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104 (2016), 148–175.
23. 23 Shackman, A.J., et al. The integration of negative affect, pain and cognitive control in the cingulate cortex. Nat. Rev. Neurosci. 12 (2011), 154–167.
24. 24 Misra, G., Coombes, S.A., Neuroimaging evidence of motor control and pain processing in the human midcingulate cortex. Cereb. Cortex 25 (2015), 1906–1919.
25. 25 Maihöfner, C., et al. Secondary somatosensory cortex is important for the sensory-discriminative dimension of pain: a functional MRI study. Eur. J. Neurosci. 23 (2006), 1377–1383.
26. 26 Bergmann, T.O., et al. Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: current approaches and future perspectives. NeuroImage 140 (2016), 4–19.
27. 27 Opitz, A., et al. Spatiotemporal structure of intracranial electric fields induced by transcranial electric stimulation in humans and nonhuman primates. Sci. Rep., 6, 2016, 31236.
28. 28 Shin, S.S., et al. Neurostimulation for traumatic brain injury. J. Neurosurg. 121 (2014), 1219–1231.
29. 29 Horvath, J.C., et al. Quantitative review finds no evidence of cognitive effects in healthy populations from single-session transcranial direct current stimulation (tDCS). Brain Stimul. Basic Transl. Clin. Res. Neuromodulation 8 (2015), 535–550.
30. 30 Brunoni, A.R., et al. Transcranial direct current stimulation for acute major depressive episodes: meta-analysis of individual patient data. Br. J. Psychiatry 208 (2016), 522–531.
31. 31 Meron, D., et al. Transcranial direct current stimulation (tDCS) in the treatment of depression: Systematic review and meta-analysis of efficacy and tolerability. Neurosci. Biobehav. Rev. 57 (2015), 46–62.
32. 32 Jones, J.P., Palmer, L.A., The two-dimensional spatial structure of simple receptive fields in cat striate cortex. J. Neurophysiol. 58 (1987), 1187–1211.
33. 33 Nelken, I., et al. In search of the best stimulus: an optimization procedure for finding efficient stimuli in the cat auditory cortex. Hear. Res. 72 (1994), 237–253.
34. 34 Birbaumer, N., et al. Slow potentials of the cerebral cortex and behavior. Physiol. Rev. 70 (1990), 1–41.
35. 35 Wolpaw, J.R., et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113 (2002), 767–791.
36. 36 Weiskopf, N., Real-time fMRI and its application to neurofeedback. NeuroImage 62 (2012), 682–692.
37. 37 Banca, P., et al. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions. J. Neural Eng., 12, 2015, 066003.
38. 38 Monti, M.M., et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362 (2010), 579–589.
39. 39 Shibata, K., et al. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334 (2011), 1413–1415.
40. 40 deBettencourt, M.T., Closed-loop training of attention with real-time brain imaging. Nat. Neurosci. 18 (2015), 470–475.
41. 41 Koizumi, A., et al. Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure. Nat. Hum. Behav., 1, 2016, 0006.
42. 42 Sitaram, R., et al. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci., 2016, 10.1038/nrn.2016.164 Published online December 22, 2016.
43. 43 Sorger, B., et al. A real-time fMRI-based spelling device immediately enabling robust motor-independent communication. Curr. Biol. 22 (2012), 1333–1338.
44. 44 Cusack, R., et al. Seeing different objects in different ways: measuring ventral visual tuning to sensory and semantic features with dynamically adaptive imaging. Hum. Brain Mapp. 33 (2012), 387–397.
45. 45 Cully, A., et al. Robots that can adapt like animals. Nature 521 (2015), 503–507.
46. 46 Ghahramani, Z., Probabilistic machine learning and artificial intelligence. Nature 521 (2015), 452–459.
47. 47 Pillow, J.W., Park, M., Adaptive Bayesian methods for closed-loop neurophysiology. El Hady, A., (eds.) Closed Loop Neuroscience, 2016, Elsevier, 3–18.
48. 48 Lewi, J., et al. Sequential optimal design of neurophysiology experiments. Neural Comput. 21 (2008), 619–687.
49. 49 Shababo, B., et al. Bayesian inference and online experimental design for mapping neural microcircuits. Advances in Neural Information Processing Systems 26 (2013), 1304–1312.
50. 50 Brochu, E., et al. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv, 2010, 1012.2599.
51. 51 Lorenz, R., et al. The automatic neuroscientist: a framework for optimizing experimental design with closed-loop real-time fMRI. NeuroImage 129 (2016), 320–334.
52. 52 Yarkoni, T., Westfall, J., Choosing prediction over explanation in psychology: lessons from machine learning. Figshare., 2016, 10.6084/m9.figshare.2441878.v1 Published online ​February 19, 2016.
53. 53 Bzdok, D., Yeo, B.T.T., The future of data analysis in the neurosciences. arXiv, 2016, 1608.03465.
54. 54 Kunert, R., Internal conceptual replications do not increase independent replication success. Psychon. Bull. Rev. 23 (2016), 1631–1638.
55. 55 Kerr, N.L., HARKing: hypothesizing after the results are known. Personal. Soc. Psychol. Rev. 2 (1998), 196–217.
56. 56 Masicampo, E.J., Lalande, D.R., A peculiar prevalence of p values just below .05. Q. J. Exp. Psychol. 65 (2012), 2271–2279.
57. 57 Myung, J.I. et al. (2009) Bayesian adaptive optimal design of psychology experiments. In Proceedings of the 2nd International Workshop in Sequential Methodologies (IWSM2009) (eds), pp. 1–6, Publisher.
58. 58 Cavagnaro, D.R., et al. Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science. Neural Comput. 22 (2009), 887–905.
59. 59 Leech, R., et al. Echoes of the brain within the posterior cingulate cortex. J. Neurosci. 32 (2012), 215–222.
60. 60 Smith, S.M., et al. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A. 106 (2009), 13040–13045.
61. 61 Laird, A.R., et al. Behavioral interpretations of intrinsic connectivity networks. J. Cogn. Neurosci. 23 (2011), 4022–4037.
62. 62 Yeo, B.T.T., et al. Functional specialization and flexibility in human association cortex. Cereb. Cortex, 26, 2014, 465.
63. 63 Lorenz, R. et al. (2016) Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization. In 2016 International Workshop on Pattern Recognition in Neuroimaging, pp. 1–4, IEEE.
64. 64 Stephan, K.E., et al. Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr. Bull. 35 (2009), 509–527.
65. 65 Kennedy, D.P., et al. Failing to deactivate: resting functional abnormalities in autism. Proc. Natl. Acad. Sci. U. S. A. 103 (2006), 8275–8280.
66. 66 Geranmayeh, F., et al. Network dysfunction predicts speech production after left hemisphere stroke. Neurology 86 (2016), 1296–1305.
67. 67 Bonnelle, V., et al. Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J. Neurosci. 31 (2011), 13442–13451.
68. 68 Minzenberg, M.J., Carter, C.S., Developing treatments for impaired cognition in schizophrenia. Trends Cogn. Sci. 16 (2012), 35–42.
69. 69 Hummel, F.C., Cohen, L.G., Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke?. Lancet Neurol. 5 (2006), 708–712.
70. 70 Rothwell, J.C., Clinical applications of noninvasive electrical stimulation: problems and potential. Clin. EEG Neurosci. 43 (2012), 209–214.
71. 71 Cabral-Calderin, Y., Transcranial alternating current stimulation affects the BOLD signal in a frequency and task-dependent manner. Hum. Brain Mapp. 37 (2015), 94–121.
72. 72 Vosskuhl, J., et al. BOLD signal effects of transcranial alternating current stimulation (tACS) in the alpha range: A concurrent tACS-fMRI study. NeuroImage 140 (2016), 118–125.
73. 73 Kim, W., et al. A hierarchical adaptive approach to optimal experimental design. Neural Comput. 26 (2014), 2465–2492.
74. 74 Gu, H., et al. A hierarchical Bayesian approach to adaptive vision testing: a case study with the contrast sensitivity function. J. Vis., 16, 2016 15–15.
75. 75 Hensman, J., et al. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinformatics, 14, 2013, 252.
76. 76 Yarkoni, T., et al. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8 (2011), 665–670.
77. 77 Poldrack, R.A., Yarkoni, T., From brain maps to cognitive ontologies: informatics and the search for mental structure. Annu. Rev. Psychol. 67 (2016), 587–612.
78. 78 Poldrack, R.A., Can cognitive processes be inferred from neuroimaging data?. Trends Cogn. Sci. 10 (2006), 59–63.
79. 79 Costafreda, S.G., Pooling fMRI Data: meta-analysis, mega-analysis and multi-center studies. Front. Neuroinformatics, 3, 2009, 33.
80. 80 Kriegeskorte, N., et al. Representational similarity analysis – connecting the branches of systems neuroscience. Front. Syst. Neurosci., 2, 2008, 4.
81. 81 Poldrack, R.A., et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci., 2017, 10.1038/nrn.2016.167 Published online January 5, 2017.
82. 82 Scargle, J.D., Publication bias: the ‘file-drawer problem’ in scientific inference. J. Sci. Explor., 2000.
83. 83 Rasmussen, C.E., Williams, C.K.I., Gaussian Processes for Machine Learning. 2006, MIT Press.
84. 84 Krüger, G., Glover, G.H., Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magn. Reson. Med. 46 (2001), 631–637.
85. 85 Hampshire, A., et al. Fractionating human intelligence. Neuron 76 (2012), 1225–1237.
86. 86 Monti, R. et al. (2016) Text-mining the neurosynth corpus using deep Boltzmann machines. In 2016 International Workshop on Pattern Recognition in Neuroimaging(Vol. 14), pp. 91–106, IEEE.
87. 87 McDonald, A., et al. The real-time fMRI neurofeedback based stratification of default network regulation neuroimaging data repository. Neuroimage 146 (2016), 157–170.
88. 88 Ruiz, S., et al. Real-time fMRI brain computer interfaces: self-regulation of single brain regions to networks. Biol. Psychol. 95 (2014), 4–20.
89. 89 Koush, Y., et al. Learning control over emotion networks through connectivity-based neurofeedback. Cereb. Cortex., 2015, 10.1093/cercor/bhv311 Published online December 17, 2016.
90. 90 Blankertz, B., et al. Single-trial analysis and classification of ERP components — a tutorial. NeuroImage 56 (2011), 814–825.
91. 91 Blankertz, B., et al. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25 (2008), 41–56.
92. 92 Hanslmayr, S., et al. Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl. Psychophysiol. Biofeedback 30 (2005), 1–10.
93. 93 Wei, Q., et al. Amplitude and phase coupling measures for feature extraction in an EEG-based brain–computer interface. J. Neural Eng., 4, 2007, 120.
94. 94 Billinger, M., et al. Single-trial connectivity estimation for classification of motor imagery data. J. Neural Eng., 10, 2013, 046006.
95. 95 Mullen, T., et al. Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2013 (2013), 2184–2187.
96. 96 Billinger, M., et al. SCoT: a Python toolbox for EEG source connectivity. Front. Neuroinformatics, 8, 2014, 22.
97. 97 Monti, R.P., et al. Real-time estimation of dynamic functional connectivity networks. Hum. Brain Mapp. 38 (2017), 202–220.
98. 98 Monti, R. et al. (2015) Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data. In 2015 International on Workshop Pattern Recognition in NeuroImaging, pp. 1–4, IEEE.
99. 99 Lorenz, R., et al. Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization. arXiv, 2015, 1511.07827.
100. 100 Bauer, P., Köhne, K., Evaluation of experiments with adaptive interim analyses. Biometrics 50 (1994), 1029–1041.
101. 101 Chow, S.-C., Chang, M., Adaptive design methods in clinical trials – a review. Orphanet J. Rare Dis., 3, 2008, 11.