Vol. 169
Latest Volume
All Volumes
PIER 180 [2024] PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2020-12-31
Distinguishing Bipolar Depression from Major Depressive Disorder Using fNIRS and Deep Neural Network
By
Progress In Electromagnetics Research, Vol. 169, 73-86, 2020
Abstract
A variety of psychological scales are utilized at present as the most important basis for clinical diagnosis of mood disorders. An experienced psychiatrist assesses and diagnoses mood disorders based on clinical symptoms and relevant assessment scores. This symptom based clinical criterion is limited by the psychiatrist's experience. In practice, it is difficult to distinguish the patients with bipolar disorder with depression episode (bipolar depression, BD) from those with major depressive disorder (MDD). The functional near-infrared spectroscopy (fNIRS) technology is commonly used to perceive the emotions of a human. It measures the hemodynamic parameters of the brain, which correlate with cerebral activation. Here, we propose a machine learning classification method based on deep neural network for the brain activations of mood disorders. Large time scale connectivity is determined using an attention long short term memory neural network and short-time feature information are considered using the InceptionTime neural network in this method. Our combined method is referred to as AttentionLSTM-InceptionTime (ALSTMIT). We collected fNIRS data of 36 MDD patients and 48 BD patients who were in the depressed state. All the patients were monitored by fNIRS during conducting the verbal fluency task (VFT). We trained the model with the ALSTMIT network. The algorithm can distinguish the two types of patients effectively: the average accuracy of classification on the test set can reach 96.2% stably. The classification can provide an objective diagnosis tool for clinicians, and this algorithm may be critical for the early detection and precise treatment for the patients with mood disorders.
Citation
Tengfei Ma, Hailong Lyu, Jingjing Liu, Yuting Xia, Chao Qian, Julian Evans, Weijuan Xu, Jianbo Hu, Shaohua Hu, and Sailing He, "Distinguishing Bipolar Depression from Major Depressive Disorder Using fNIRS and Deep Neural Network," Progress In Electromagnetics Research, Vol. 169, 73-86, 2020.
doi:10.2528/PIER20102202
References

1. Bahdanau, D., K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," ICLR 2015, 2015.

2. Barreiros, A. R., I. A. Breukelaar, W. Chen, M. Erlinger, and M. S. Korgaonkar, "Neurophysiological markers of attention distinguish bipolar disorder and unipolar depression," Journal of Affective Disorders, Vol. 274, 411-419, 2020.

3. Benavides-Varela, S., D. M. Gomez, and J. Mehler, "Studying neonates' language and memory capacities with functional near-infrared spectroscopy," Frontiers in Psychology, Vol. 2, 64, 2011.

4. Boas, D. A., C. E. Elwell, M. Ferrari, and G. Taga, "Twenty years of functional near-infrared spectroscopy: Introduction for the special issue," NeuroImage, Vol. 85, 1-5, 2014.

5. Cerullo, M. A., et al., "Bipolar I disorder and major depressive disorder show similar brain activation during depression," Bipolar Disorders, Vol. 16, No. 7, 703-712, 2015.

6. Chen, X., Z. Wei, M. Li, and P. Rocca, "A review of deep learning approaches for inverse scattering problems (invited review)," Progress In Electromagnetics Research, Vol. 167, 67-81, 2020.

7. Cho, K., et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724-1734, 2014.

8. Cristia, A., et al., "An online database of infant functional near infrared spectroscopy studies: A community-augmented systematic review," PloS One, Vol. 8, No. 3, e58906, 2013.

9. Dieler, A. C., S. V. Tupak, and A. J. Fallgatter, "Functional near-infrared spectroscopy for the assessment of speech related tasks," Brain & Language, Vol. 121, No. 2, 90-109, 2012.

10. Ehlis, A.-C., S. Schneider, T. Dresler, and A. J. Fallgatter, "Application of functional near-infrared spectroscopy in psychiatry," NeuroImage, Vol. 85, 478-488, 2014.

11. Fawaz, H. I., et al., "InceptionTime: Finding alexnet for time series classification," Data Mining and Knowledge Discovery, Vol. 34, 1936-1962, 2020.

12. He, K., X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778, 2016.

13. Hochreiter, S. and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol. 9, No. 8, 1735-1780, 1997.

14. Hoshi, Y. and M. Tamura, "Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in man," Neuroscience Letters, Vol. 150, No. 1, 5-8, 1993.

15. Ioffe, S. and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," Proceedings of the 32nd International Conference on Machine Learning, PMLR, Vol. 37, 448-456, 2015.

16. Jobsis, F. F., "Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters," Science, Vol. 198, No. 4323, 1264-1267, 1977.

17. Jobsis-vander Vliet, F. F., "Discovery of the near-infrared window into the body and the early development of near-infrared spectroscopy," Journal of Biomedical Optics, Vol. 4, No. 4, 392-396, 1999.

18. Karim, F., S. Majumdar, H. Darabi, and S. Chen, "LSTM fully convolutional networks for time series classification," IEEE Access, Vol. 6, No. 99, 1662-1669, 2018.

19. Kato, T., A. Kamei, S. Takashima, and T. Ozaki, "Human visual cortical function during photic stimulation monitoring by means of near-infrared spectroscopy," Journal of Cerebral Blood Flow & Metabolism, Vol. 13, No. 3, 516-520, 1993.

20. Kawakami, K., Supervised sequence labelling with recurrent neural networks, Ph.D. dissertation, 2008.

21. Kim, Y.-K. and K.-S. Na, "Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective," Progress in Neuropsychopharmacology and Biological Psychiatry, Vol. 80, 71-80, 2018.

22. Kopton, I. M. and P. Kenning, "Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research," Frontiers in Human Neuroscience, Vol. 8, 549, 2014.

23. LeCun, Y., Y. Bengio, et al. "Convolutional networks for images, speech, and time series," The Handbook of Brain Theory and Neural Networks, Vol. 3361, No. 10, 1995, 1995.

24. Li, Z., Y. Wang, W. Quan, T. Wu, and B. Lv, "Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy," Journal of Neuroence Methods, Vol. 241, 101-110, 2015.

25. Li, Z., Y. Wang, W. Quan, T. Wu, and B. Lv, "Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy," Journal of Neuroscience Methods, Vol. 241, 101-110, 2015.

26. Maalouf, F. T., et al., "Impaired sustained attention and executive dysfunction: Bipolar disorder versus depression-specific markers of affective disorders," Neuropsychologia, Vol. 48, No. 6, 1862-1868, 2010.

27. McIntyre, R. S., M. Berk, E. Brietzke, B. I. Goldstein, C. L¶opez-Jaramillo, L. V. Kessing, G. S. Malhi, A. A. Nierenberg, J. D. Rosenblat, A. Majeed, et al., "Bipolar disorders," The Lancet, Vol. 396, No. 10265, 1841-1856, 2020.

28. Molavi, B., L. May, J. Gervain, M. Carreiras, J. F. Werker, and G. A. Dumont, "Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy," Frontiers in Human Neuroscience, Vol. 7, 921, 2014.

29. Naseer, N. and K.-S. Hong, "fNIRS-based brain-computer interfaces: A review," Frontiers in Human Neuroscience, Vol. 9, 3, 2015.

30. Nguyen, D. K., et al., "Non-invasive continuous EEG-fNIRS recording of temporal lobe seizures," Epilepsy Research, Vol. 99, No. 1-2, 112-126, 2012.

31. Obrig, H., "Nirs in clinical neurology --- A `promising' tool?," NeuroImage, Vol. 85, 535-546, 2014.

32. O'Halloran, M., B. McGinley, R. C. Conceicao, F. Morgan, E. Jones, and M. Glavin, "Spiking neural networks for breast cancer classification in a dielectrically heterogeneous breast," Progress In Electromagnetics Research, Vol. 113, 413-428, 2011.

33. Onishi, A., H. Furutani, T. Hiroyasu, and S. Hiwa, "An fNIRS study of brain state during letter and category uency tasks," Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 4, 228-231, 2019.

34. Pascanu, R., C. Gulcehre, K. Cho, and Y. Bengio, "How to construct deep recurrent neural networks," Proceedings of the Second International Conference on Learning Representations (ICLR 2014), 2014.

35. Phillips, M. L. and D. J. Kupfer, "Bipolar disorder diagnosis: Challenges and future directions," Lancet, Vol. 381, No. 9878, 1663-1671, 2013.

36. Quan, W., T. Wu, Z. Li, Y. Wang, W. Dong, and B. Lv, "Reduced prefrontal activation during a verbal fluency task in chinese-speaking patients with schizophrenia as measured by near-infrared spectroscopy," Progress in Neuropsychopharmacology and Biological Psychiatry, Vol. 58, 51-58, 2015.

37. Quaresima, V., S. Bisconti, and M. Ferrari, "A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults," Brain and Language, Vol. 121, No. 2, 79-89, 2012.

38. Raucher-Chene, D., A. M. Achim, A. Kaladjian, and C. Besche-Richard, "Verbal uency in bipolar disorders: A systematic review and meta-analysis," Journal of Affective Disorders, Vol. 207, 359-366, 2017.

39. Santosa, H., M. J. Hong, and K.-S. Hong, "Lateralization of music processing with noises in the auditory cortex: An fNIRS study," Frontiers in Behavioral Neuroscience, Vol. 8, 418, 2014.

40. Sitaram, R., A. Caria, and N. Birbaumer, "Hemodynamic brain-computer interfaces for communication and rehabilitation," Neural Networks, Vol. 22, No. 9, 1320-1328, 2009.

41. Suto, T., M. Fukuda, M. Ito, T. Uehara, and M. Mikuni, "Multichannel near-infrared spectroscopy in depression and schizophrenia: Cognitive brain activation study," Biological Psychiatry, Vol. 55, No. 5, 501-511, 2004.

42. Luong, M.-T., H. Pham, and C. D. Manning, "Effective approaches to attention-based neural machine translation," Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 1412-1421, Lisbon, Portugal, September 17-21, 2015.

43. Szegedy, C., et al., "Going deeper with convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9, 2015.

44. Szegedy, C., S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, inception-resnet and the impact of residual connections on learning," Thirty-first AAAI Conference on Artificial Intelligence, 2017.

45. Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818-2826, 2016.

46. Tomioka, H., et al., "A longitudinal functional neuroimaging study in medication-nave depression after antidepressant treatment," PLoS One, Vol. 10, No. 3, e0120828, 2015.

47. Tomioka, H., B. Yamagata, S. Kawasaki, S. Pu, A. Iwanami, J. Hirano, K. Nakagome, and M. Mimura, "A longitudinal functional neuroimaging study in medication-naive depression after antidepressant treatment," PLoS One, Vol. 10, No. 3, e0120828, 2015.

48. Villringer, A., J. Planck, C. Hock, L. Schleinkofer, and U. Dirnagl, "Near infrared spectroscopy (NIRS): A new tool to study hemodynamic changes during activation of brain function in human adults," Neuroscience Letters, Vol. 154, No. 1-2, 101-104, 1993.

49. Wang, S., Y. Zhang, T. Zhan, P. Phillips, Y.-D. Zhang, G. Liu, S. Lu, and X. Wu, "Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning (invited review)," Progress In Electromagnetics Research, Vol. 156, 105-133, 2016.

50. Watanabe, E., Y. Nagahori, and Y. Mayanagi, "Focus diagnosis of epilepsy using near-infrared spectroscopy," Epilepsia, Vol. 43, 50-55, 2002.

51. Wise, T., J. Radua, G. Nortje, A. J. Cleare, A. H. Young, and D. Arnone, "Voxel-based meta-analytical evidence of structural disconnectivity in major depression and bipolar disorder," Biological Psychiatry, 2016.

52. Wolfe, J., E. Granholm, N. Butters, E. Saunders, and D. Janowsky, "Verbal memory deficits associated with major affective disorders: A comparison of unipolar and bipolar patients," Journal of Affective Disorders, Vol. 13, No. 1, 83-92, 1987.