Skip to main content
SpringerLink
Log in

Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data

  • Chapter
  • First Online:
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 7))

Abstract

This chapter introduces a new method for the integration of time-space data with additional and sometimes, a priory existing information, about the orientation (direction) of the spread of the temporal information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R.C. DeCharms, Application of real-time fMRI. Nat. Rev. Neurosci. 9, 720–729 (2008)

    Article  Google Scholar 

  2. J.P. Mitchell, C.N. Macrae, M.R. Banaji, Encoding specific effects of social cognition on the neural correlates of subsequent memory. J. Neurosci. 24(21), 4912–4917 (2004)

    Article  Google Scholar 

  3. K.H. Brodersen, K. Wiech, E.I. Lomakina, C.S. Lin, J.M. Buhmann, U. Bingel, I. Tracey, Decoding the perception of pain from fMRI using multivariate pattern analysis. NeuroImage 63(3), 1162–1170 (2012). https://doi.org/10.1016/j.neuroimage.2012.08.035

    Article  Google Scholar 

  4. R.B. Buxton, K. Uludağ, D.J. Dubowitz, T.T. Liu, Modeling the hemodynamic response to brain activation. NeuroImage 23(Suppl 1), S220–S233 (2004). https://doi.org/10.1016/j.neuroimage.2004.07.013

    Article  Google Scholar 

  5. S. Ogawa, T.M. Lee, A.R. Kay, D.W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci. 87(24), 9868–9872 (1990)

    Article  Google Scholar 

  6. J.V. Haxby, M.I. Gobbini, M.L. Furey, A. Ishai, J.L. Schouten, P. Pietrini, Distributed and overlapping representation of faces and objects in ventral temporal cortex. Science 293(5539), 2425–2430 (2001)

    Article  Google Scholar 

  7. M.A. Lindquist, The statistical analysis of fMRI data. Stat. Sci. 23(4), 439–464 (2008). https://doi.org/10.1214/09-STS282

    Article  MathSciNet  MATH  Google Scholar 

  8. K.J. Friston, C.D. Frith, R.S. Frackowiak, R. Turner, Characterizing dynamic brain responses with fMRI: a multivariate approach. NeuroImage (1995). Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9343599

  9. M.K. Carroll, G.A. Cecchi, I. Rish, R. Garg, A.R. Rao, Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44(1), 112–122 (2009)

    Article  Google Scholar 

  10. T. Schmah, R.S. Zemel, G.E. Hinton, S.L. Small, S.C. Strother, Comparing classification methods for longitudinal fMRI studies. Neural Comput. 22(11), 2729–2762 (2010). https://doi.org/10.1162/NECO_a_00024

    Article  MATH  Google Scholar 

  11. D.D. Cox, R.L. Savoy, Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage, 19(2 Pt 1), 261–270 (2003)

    Google Scholar 

  12. Y. Kamitani, F. Tong, Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8(5), 679–85 (2005)

    Google Scholar 

  13. M. Misaki, Y. Kim, P.A. Bandettini, N. Kriegeskorte, Comparions of multivariate classifiers and response normalizations for pattern-information fMRI. NeuroImage 53(1), 103–118 (2010). https://doi.org/10.1016/j.neuroimage.2010.05.051.Comparison

    Article  Google Scholar 

  14. J. Mourão-Miranda, A.L.W. Bokde, C. Born, H. Hampel, M. Stetter, Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 28(4), 980–995 (2005). https://doi.org/10.1016/j.neuroimage.2005.06.070

    Article  Google Scholar 

  15. T.M. Mitchell, R. Hutchinson, M.A. Just, R.S. Niculescu, F. Pereira, X. Wang, Classifying Instantaneous Cognitive States from FMRI Data. AMIA, in Annual Symposium Proceedings/AMIA Symposium. AMIA Symposium (2003), pp. 465–469. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1479944&tool=pmcentrez&rendertype=abstract

  16. I. Rustandi, Classifying Multiple-Subject fMRI Data Using the Hierarchical Gaussian Naïve Bayes Classifier, in 13th Conference on Human Brain Mapping (2007a), pp. 4–5

    Google Scholar 

  17. I. Rustandi, Hierarchical Gaussian Naive Bayes classifier for multiple-subject fMRI data. Submitted to AISTATS (1), 2–4 (2007b)

    Google Scholar 

  18. S.M. Polyn, G.J. Detre, S. Takerkart, V.S. Natu, M.S. Benharrosh, B.D. Singer, J.D. Cohen, J.V. Haxby, K.A. Norman, A Matlab-based toolbox to facilitate multi-voxel pattern classification of fMRI data, 2005

    Google Scholar 

  19. Y. Fan, D. Shen, C. Davatzikos, Detecting cognitive states from fMRI images by machine learning and multivariate classification, in Conference on Computer Vision and Pattern Recognition Workshop. IEEE (2006), pp. 89–89 https://doi.org/10.1109/cvprw.2006.64

  20. S.J. Hanson, T. Matsuka, J.V. Haxby, Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a “face” area? NeuroImage 23(1), 156–166 (2004). https://doi.org/10.1016/j.neuroimage.2004.05.020

    Article  Google Scholar 

  21. G. Yourganov, T. Schmah, N.W. Churchill, M.G. Berman, C.L. Grady, S.C. Strother, Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks. NeuroImage 96, 117–132 (2014). https://doi.org/10.1016/j.neuroimage.2014.03.074

    Article  Google Scholar 

  22. J.D. Haynes, G. Rees, Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat. Neurosci. 8(5), 686–691 (2005). https://doi.org/10.1038/nn1445

    Article  Google Scholar 

  23. S. Ku, A. Gretton, J. Macke, N.K. Logothetis, Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. Magn. Reson. Imaging 26(7), 1007–1014 (2008). https://doi.org/10.1016/j.mri.2008.02.016

    Article  Google Scholar 

  24. T. Schmah, G.E. Hinton, R.S. Zemel, S.L. Small, S. Strother, Generative versus discriminative training of RBMs for classification of fMRI images, in Advances in Neural Information Processing Systems, 21, ed. by D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (MIT Press, Cambridge, MA, 2009), pp. 1409–1416

    Google Scholar 

  25. S. LaConte, S. Strother, V. Cherkassky, J. Anderson, X. Hu, Support vector machines for temporal classification of block design fMRI data. NeuroImage 26(2), 317–329 (2005). https://doi.org/10.1016/j.neuroimage.2005.01.048

    Article  Google Scholar 

  26. N. Mørch, L. Hansen, S. Strother, C. Svarer, D. Rottenberg, B. Lautrup, Nonlinear Versus Linear Models in Functional Neuroimaging: Learning Curves and Generalization Crossover, in Proceedings of the 15th International Conference on Information Processing in Medical Imaging, vol. 1230 of Lecture Notes in Computer Science. Springer (1997), pp. 259–270

    Google Scholar 

  27. J. Mourão-Miranda, K.J. Friston, M. Brammer, Dynamic discrimination analysis: a spatial-temporal SVM. NeuroImage 36(1), 88–99 (2007). https://doi.org/10.1016/j.neuroimage.2007.02.020

    Article  Google Scholar 

  28. M.A.J. Van Gerven, B. Cseke, F.P. de Lange, T. Heskes, Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. NeuroImage 50(1), 150–161 (2010). https://doi.org/10.1016/j.neuroimage.2009.11.064

    Article  Google Scholar 

  29. B. Ng, R. Abugharbieh, Modeling Spatiotemporal Structure in fMRI Brain Decoding Using Generalized Sparse Classifiers, in 2011 International Workshop on Pattern Recognition in NeuroImaging (2011b), pp. 65–68. https://doi.org/10.1109/prni.2011.10

  30. P. Avesani, H. Hazan, E. Koilis, L. Manevitz and D. Sona, Learning BOLD Response in fMRI by Reservoir Computing, in 2011 International Workshop on Pattern Recognition in NeuroImaging, (2011), pp. 57–60. https://doi.org/10.1109/prni.2011.16

  31. J. Sui, T. Adalı, Q. Yu, J. Chen, V.D. Calhoun, A review of multivariate methods for multimodal fusion of brain imaging data. J. Neurosci. Methods, 204(1), 68–81 (2012)

    Google Scholar 

  32. M.J. Patel, C. Andreescu, J.C. Price, K.L. Edelman, C.F. Reynolds, III, H.J. Aizenstein, Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int. J. Geriatric Psychiatry 30(10), 1056–1067 (2015)

    Google Scholar 

  33. A. Khodayari-Rostamabad, J.P. Reilly, G.M. Hasey, H. de Bruin, D.J. MacCrimmon, A machine learning approach using EEG data to predict response to ssri treatment for major depressive disorder. Clin. Neurophysiol. 124(10), 1975–1985 (2013)

    Google Scholar 

  34. A. Khodayari-Rostamabad, G.M. Hasey, D.J. MacCrimmon, J.P. Reilly, H. de Bruin, A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clin. Neurophysiol. 121(12), 1998–2006 (2010)

    Article  Google Scholar 

  35. C.-C. Lin et al., Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data. Comput. Methods Programs Biomed. 91(2), 91–99 (2008)

    Article  Google Scholar 

  36. O. Doehrmann et al., Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry 70(1), 87–97 (2013)

    Google Scholar 

  37. M.D. Greicius, K. Supekar, V. Menon, R.F. Dougherty, Restingstate functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19(1), 72–78 (2009)

    Google Scholar 

  38. K.E. Stephan, K.J. Friston, C.D. Frith, Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophrenia Bull. 35(3), 509–527 (2009)

    Google Scholar 

  39. P.A. Valdes-Sosa et al., Model driven EEG/fMRI fusion of brain oscillations. Hum. Brain Mapping, 30(9), 2701–2721 (2009)

    Google Scholar 

  40. S. M. Plis et al., Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Comput. Biol. Med. 41(12), 1156–1165 (2011)

    Google Scholar 

  41. H. Yang, J. Liu, J. Sui, G. Pearlson, V.D. Calhoun, A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Frontiers Hum. Neurosci. 4(192), 3389 (2010)

    Google Scholar 

  42. V. D. Calhoun, J. Liu, T. Adalı, A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage, 45(1), S163–S172 (2009)

    Google Scholar 

  43. S.J. Teipel, A.L. Bokde, T. Meindl, E. Amaro Jr, J. Soldner, M.F. Reiser, S.C. Herpertz, H.J. Möller, H. Hampel, White matter microstructure underlying default mode network connectivity in the human brain. NeuroImage, 49(3), 2021–2032 (2010)

    Google Scholar 

  44. N.M. Correa, Y.O. Li, T. Adali, V.D. Calhoun, Canonical correlation analysis for feature-based fusion of biomedical imaging modalities and its application to detection of associative networks in schizophrenia. IEEE J. Sel. Topics Signal Process. 2(6), 998–1007 (2008)

    Google Scholar 

  45. N.M. Correa, T. Eichele, T. Adalı, Y.-O. Li, V.D. Calhoun, Multiset canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI. NeuroImage, 50(4), 1438–1445 (2010)

    Google Scholar 

  46. N. Correa, Y.-O. Li, T. Adalı, V.D. Calhoun, Examining Associations Between fMRI and EEG Data Using Canonical Correlation Analysis, in Proceedings 5th IEEE International Symposium on Biomedical Imaging, Nano Macro (ISBI), May 2008, pp. 1251–1254

    Google Scholar 

  47. K. Chen et al., Linking functional and structural brain images with multivariate network analyses: a novel application of the partial least square method. NeuroImage, 47(2), 602–610 (2009)

    Google Scholar 

  48. N.K. Kasabov, NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 52, 62–76 (2014)

    Google Scholar 

  49. N. Kasabov, M. Doborjeh, Z. Doborjeh, Mapping, learning, visualization, classification, and understanding of fMRI Data in the NeuCube evolving spatiotemporal data machine of spiking neural networks, in IEEE Transactions of Neural Networks and Learning Systems, https://doi.org/10.1109/tnnls.2016.2612890 Manuscript Number: TNNLS-2016-P-6356, 2016

  50. N. Kasabov, To spike or not to spike: a probabilistic spiking neuron model. Neural Netw. 23(1), 16–19 (2010). https://doi.org/10.1016/j.neunet.2009.08.010

    Article  Google Scholar 

  51. M. Åberg, L. Löken, J. Wessberg, An evolutionary approach to multivariate feature selection for fMRI pattern analysis (2008)

    Google Scholar 

  52. T. Niiniskorpi, M. Bj, J. Wessberg, Particle swarm feature selection for fMRI pattern classification, in BIOSIGNALS (2009), pp. 279–284

    Google Scholar 

  53. J. Kennedy, R. Eberhart, Particle Swarm Optimization, in Proceedings of ICNN’95—International Conference on Neural Networks, vol. 4 (1995), pp. 1942–1948. https://doi.org/10.1109/icnn.1995.488968

  54. C. Koch, R.C. Reid, Observatories of the mind. Nature 483(22 March 2012), 397–398 (2012). https://doi.org/10.1038/483397a

  55. X. Wang, J. Yang, X. Teng, W. Xia, R. Jensen, Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28(4), 459–471 (2007). https://doi.org/10.1016/j.patrec.2006.09.003

    Article  Google Scholar 

  56. S. Ryali, K. Supekar, D.A. Abrams, V. Menon, Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage 51(2), 752–764 (2010)

    Google Scholar 

  57. O. Yamashita, M. Sato, T. Yoshioka, F. Tong, Y. Kamitani, Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. NeuroImage 42(4), 1414–1429 (2008). https://doi.org/10.1016/j.neuroimage.2008.05.050

    Article  Google Scholar 

  58. B. Ng, A. Vahdat, G. Hamarneh, R. Abugharbieh, Generalized sparse classifiers for decoding cognitive states in fMRI, in Machine Learning in Medical Imaging (Springer, Berlin, 2010), pp. 108–115

    Google Scholar 

  59. B. Ng, R. Abugharbieh, Generalized group sparse classifiers with application in fMRI brain decoding. Cvpr 2011, 1065–1071 (2011). https://doi.org/10.1109/CVPR.2011.5995651

    Article  Google Scholar 

  60. R. Brette et al., Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23(3), 349–398 (2007)

    Google Scholar 

  61. E.M. Izhikevich, Polychronization: computation with spikes. Neural Comput. 18(2), 245–282 (2006)

    Google Scholar 

  62. N. Scott, N. Kasabov, G. Indiveri, NeuCube Neuromorphic Framework for Spatio-Temporal Brain Data and Its Python Implementation, in Proceedings ICONIP, vol. 8228 (Springer, LNCS) (2013), pp. 78–84

    Google Scholar 

  63. S.B. Furber, F. Galluppi, S. Temple, L.A. Plana, The SpiNNaker project. Proc. IEEE 102(5), 652–665 (2014)

    Google Scholar 

  64. P. A. Merolla et al., A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014)

    Google Scholar 

  65. G. Indiveri et al., Neuromorphic silicon neuron circuits, Frontiers Neurosci. May 2011. [Online]. Available: http://dx.doi.org/10.3389/fnins.2011.00073

  66. A. van Schaik, S.-C. Liu, AER EAR: A Matched Silicon Cochlea Pair with Address Event Representation Interface, in Proceedings IEEE International Symposium Circuits System, vol. 5. May 2005, pp. 4213–4216

    Google Scholar 

  67. T. Delbruck, jAER. [Online]. Available: http://sourceforge.net. Accessed 15 Oct 2014

  68. P. Lichtsteiner, C. Posch, T. Delbruck, A 128 × 128 120 dB 15 using latency asynchronous temporal contrast vision sensor. IEEE J. SolidState Circuits 43(2), 566–576 (2008)

    Google Scholar 

  69. S. Song, K.D. Miller, L.F. Abbott, Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)

    Google Scholar 

  70. N. Kasabov, K. Dhoble, N. Nuntalid, G. Indiveri, Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Netw. 41, 188–201 (2013)

    Google Scholar 

  71. N. Kasabov, Evolving Connectionist Systems (Springer, New York, 2007), p. 2007

    MATH  Google Scholar 

  72. S.G. Wysoski, L. Benuskova, N. Kasabov, Evolving spiking neural networks for audiovisual information processing. Neural Netw. 23(7), 819–835. (2010)

    Google Scholar 

  73. W. Maass, T. Natschläger, H. Markram, Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)

    Google Scholar 

  74. J. Talairach, P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging (Thieme Medical Publishers, New York, 1998), p. 1988

    Google Scholar 

  75. M. Brett, K. Christoff, R. Cusack, J. Lancaster, Using the Talairach atlas with the MNI template. NeuroImage 13(6), 85 (2001)

    Google Scholar 

  76. N. Kasabov et al., Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016). https://doi.org/10.1016/j.neunet.2015.09.011.2015

    Article  Google Scholar 

  77. E.M. Izhikevich, Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)

    Google Scholar 

  78. E. Tu et al., NeuCube(ST) for Spatio-Temporal Data Predictive Modelling with a Case Study on Ecological Data, in Proceedings International Joint Conference on Neural Networks (IJCNN), Beijing, China, July 2014, pp. 638–645

    Google Scholar 

  79. E. Tu, N. Kasabov, J. Yang, Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modeling, and understanding of stream data. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–13 (2016). https://doi.org/10.1109/tnnls.2016.2536742.2016

  80. E. Bullmore, O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10(3), 186–198 (2009)

    Article  Google Scholar 

  81. V. Braitenberg, A. Schuz, Cortex: Statistics and Geometry of Neuronal Connectivity (Springer, Berlin, 1998)

    Book  Google Scholar 

  82. S. Schliebs, N. Kasabov, Evolving spiking neural network—a survey. Evolving Syst. 4(2), 87–98 (2013)

    Google Scholar 

  83. M. Lukoševiˇcius, H. Jaeger, Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)

    Google Scholar 

  84. B. Schrauwen, J. Van Campenhout, BSA, a Fast and Accurate Spike Train Encoding Scheme, in Proceedings International Joint Conference on Neural Networks (IJCNN), vol. 4. July 2003, pp. 2825–2830

    Google Scholar 

  85. N. Sengupta, N. Scott, N. Kasabov, Framework for Knowledge Driven Optimisation Based Data Encoding for Brain Data Modelling Using Spiking Neural Network Architecture, in Proceedings 5th International Conference on Fuzzy and Neural Computing (FANCCO), (2015), pp. 109–118

    Google Scholar 

  86. N. Sengupta, N. Kasabov, Spike-time encoding as a data compression technique for pattern recognition of temporal data. Inf. Sci. 406–407, 133–145 (2017)

    Google Scholar 

  87. T. Kohonen, The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Google Scholar 

  88. N. Kasabov et al., Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke. Neurocomputing 134, 269–279 (2014)

    Google Scholar 

  89. E. Tu et al., NeuCube(ST) for Spatio-Temporal Data Predictive Modelling with a Case Study on Ecological Data, in Proceedings International Joint Conference on Neural Networks (IJCNN), July 2014, pp. 638–645

    Google Scholar 

  90. D.O. Hebb, The Organization of Behavior: A Neuropsychological Approach (Wiley, Hoboken, NJ, USA, 1949), p. 1949

    Google Scholar 

  91. W. Gerstner, R. Kempter, J. L. van Hemmen, H. Wagner, A neuronal learning rule for sub-millisecond temporal coding. Nature 383(6595), 76–78 (1996)

    Google Scholar 

  92. J. Sjöström, W. Gerstner, Spike-timing dependent plasticity. Front. Synaptic Neurosci. 5(2), 35–44 (2010)

    Google Scholar 

  93. E. van Aart, N. Sepasian, A. Jalba, A. Vilanova, CUDA-accelerated geodesic ray-tracing for fiber tracking. J. Biomed. Imag. 2011, (2011). https://doi.org/10.1155/2011/698908. Art. no. 6

  94. M. Rubinov, O. Sporns, Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)

    Google Scholar 

  95. M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith, FSL. NeuroImage, 62(2), 782–790 (2012)

    Google Scholar 

  96. V. Fonov et al., Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1), 313–327 (2011)

    Google Scholar 

  97. V. Fonov, A.C. Evans, K. Botteron, C.R. Almli, R.C. McKinstry, D.L. Collins, Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1), 313–327 (2011). [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1053811910010062, https://doi.org/10.1016/j.neuroimage. 2010.07.033

  98. R.H.R. Pruim, M. Mennes, J.K. Buitelaar, C.F. Beckmann, Evaluation of ICA-AROMA and alternative strategies for motion artefact removal in resting state fMRI. NeuroImage 112, 278–287 (2015)

    Google Scholar 

  99. R.H.R. Pruim, M. Mennes, D. van Rooij, A. Llera, J.K. Buitelaar, C.F. Beckmann, ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage 112, 267–277 (2015)

    Google Scholar 

  100. N. Kasabov et al. (2016), Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016)

    Google Scholar 

  101. A. Ng, Sparse autoencoder. CS294A Lect. Notes 72, 1–19 (2011)

    Google Scholar 

  102. F. Chollet et al., Keras. [Online]. Available: https://github.com/fchollet/keras

  103. F. Pedregosa et al., Scikit-learn: machine learning in Python, J. Mach. Learn. Res. 12, 2825–2830 (2011)

    Google Scholar 

  104. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Google Scholar 

  105. T. Tieleman, Training Restricted Boltzmann Machines Using Approximations to the Likelihood Gradient, in Proceedings 25th International Conference on Machine Learning (2008), pp. 1064–1071

    Google Scholar 

  106. S. Hochreiter, J. Schmidhuber, LSTM Can Solve Hard Long Time Lag Problems, in Proceedings Advances Neural Information Processing Systems (1997), pp. 473–479

    Google Scholar 

  107. K. Cho et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation. [Online]. Available: https://arxiv.org/abs/1406.1078

  108. F.A. Middleton, P.L. Strick, Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science 266(5184), 458–461 (1994). [Online]. Available: http://science.sciencemag.org/content/266/5184/458, https://doi.org/10.1126/science.7939688

  109. H. Baillieux, W. Verslegers, P. Paquier, P.P. De Deyn, P. Mariën, Cerebellar cognitive affective syndrome associated with topiramate. Clin. Neurol. Neurosurg. 110(5), 496–499 (2008)

    Google Scholar 

  110. J.-H. Gao, L.M. Parsons, J.M. Bower, J. Xiong, J. Li, P.T. Fox, Cerebellum implicated in sensory acquisition and discrimination rather than motor control. Science 272(5261), 545–547 (1996)

    Google Scholar 

  111. E. Courchesne, N.A. Akshoomoff, J. Townsend, O. Saitoh, A model system for the study of attention and the cerebellum: infantile autism. Electroencephalogr. Clin. Neurophysiol. Suppl. 44, 315–325 (1995)

    Google Scholar 

  112. N. Kasabov, L. Zhou, M. Gholami Doborjeh, J. Yang, New algorithms for encoding, learning and classification of fMRI Data in a spiking neural network architecture: A case on modelling and understanding of dynamic cognitive processes. in IEEE Transaction on Cognitive and Developmental Systems, 2017. https://doi.org/10.1109/TCDS.2016.2636291

  113. N. Sengupta, C. McNabb, N. Kasabov, B. Russel, Integrating space, time and orientation in spiking neural networks: a case study on multimodal brain data modelling, in IEEE Tr NNLS, 2018. https://ieeexplore.ieee.org/document/8291047/

  114. Medical Image Computing, [Online]. Available: https://en.wikipedia.org/wiki/Medical_image_computing. Accessed 31 Jan 2018

Download references

Acknowledgements

Some of the material in this chapter was first published in [113]. I would like to acknowledge the contribution of my co-authors Neelava Sengupta, C. McNabb and B. Russel and especially the first author of the paper Neel.

Author information

Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand

    Nikola K. Kasabov

Authors
  1. Nikola K. Kasabov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikola K. Kasabov .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kasabov, N.K. (2019). Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_11

Download citation

Publish with us

Policies and ethics

Search

Navigation