Objective Discrimination of Depression: Detection and Analysis of Resting State Functional Connectivity Based on Optical Brain Imaging
Zhu Huilin1, 2*, Xu Jie2,3, Li Jiangxue4, Peng Hongjun5
1 Children Developmental & Behavioral Center, Third Affiliated Hospital of Sun Yet-Sen University, Guangzhou 510630, China 2 Centre for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics,South China Normal University, Guangzhou 510006, China 3 Guangdong Branch, China Unicom Co., Ltd, Guangzhou 510627, China 4 The Research Center of Psychological Counseling, South China Normal University, Guangzhou 510631, China 5 The Department of Clinical Psychology, Guangzhou Brain Hospital Guangzhou Huiai Hospital, the Affiliated Brain Hospital of Guangzhou Medical School, Guangzhou 510170, China
Abstract:Recently, resting-state functional connectivity (RSFC) has gradually been studied in patients with mental disorders by functional near-infrared spectroscopy (fNIRS). However, it is still unknown whether RSFC derived from fNIRS is predictable for depressive disorders. In this work, we employed fNIRS(42 channels) to measure 8-minute spontaneous hemodynamic activity in the prefrontal cortex (PFC) of 28 patients having depressive disorders and 30 healthy controls. After filtering irrelative components by independent component and band-pass filter (0.008-0.09 Hz), we calculated left-right correlations in the prefrontal cortex which included inferior prefrontal cortex (IFG), middle prefrontal cortex (MFG) and superior prefrontal cortex (SFG).Then we selected two significant parameters (left-right correlations in the IFG and MFG as a participant’s two features for further classification (75% of the participants) and prediction (25% of the participants) using linear discriminant analysis (LDA) and support vector machine (SVM). Finally, a sensitivity of 73-74% and specificity of 83-87%was yielded. These results supported that RSFC derived from fNIRS is a feasible and effective technique to identify whether someone is suffered from depressive disorders.
朱绘霖,许洁,李江雪,彭红军. 抑郁症的客观判别:基于光学脑成像的静息态功能性连接检测和分析[J]. 中国生物医学工程学报, 2018, 37(3): 283-289.
Zhu Huilin, Xu Jie, Li Jiangxue, Peng Hongjun. Objective Discrimination of Depression: Detection and Analysis of Resting State Functional Connectivity Based on Optical Brain Imaging. Chinese Journal of Biomedical Engineering, 2018, 37(3): 283-289.
[1] Beauregard M, Leroux JM, Bergman S, et al. The functional neuroanatomy of major depression: an fMRI study using an emotional activation paradigm[J]. Neuroreport, 1998, 9(14): 3253-3258. [2] Drevets WC. Functional anatomical abnormalities in limbic and prefrontal cortical structures in major depression[J]. Progress in Brain Research, 2000, 126: 413-431. [3] Robbins TW. Controlling stress:How the brain protects itself from depression[J]. Nature Neuroscience, 2005, 8(3): 261-262. [4] Zeng LingLi, Shen Hui, Liu Li, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis[J]. Brain, 2012, 135(5): 1498-1507. [5] Drevets WC, Price JL, Simpson JR, et al. Subgenual prefrontal cortex abnormalities in mood disorders[J]. Nature, 1997, 386: 824-827. [6] Adler CM, Holland SK, Schmithorst V, et al. Abnormal frontal white matter tracts in bipolar disorder: a diffusion tensor imaging study[J]. Bipolar Disorders, 2004, 6(3): 197-203. [7] Nobuhara K, Okugawa G, Sugimoto T, et al. Frontal white matter anisotropy and symptom severity of late-life depression: a magnetic resonance diffusion tensor imaging study[J]. Journal of Neurology, Neurosurgery & Psychiatry, 2006, 77(1): 120-122. [8] Harvey PO, Fossati P, Pochon JB, et al. Cognitive control and brain resources in major depression: an fMRI study using the n-back task[J]. Neuroimage, 2005, 26(3): 860-869. [9] Jobsis FF. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters[J]. Science, 1977, 198(4323): 1264-1267. [10] Vanderwert RE, Nelson CA. The use of near-infrared spectroscopy in the study of typical and atypical development[J]. Neuroimage, 2014, 85: 264-271. [11] Hoshi Y. Functional near‐infrared optical imaging: Utility and limitations in human brain mapping[J]. Psychophysiology, 2003, 40(4): 511-520. [12] Hillman E M. Optical brain imaging in vivo:Techniques and applications from animal to man[J]. Journal of Biomedical Optics, 2007, 12(5): 051402-051402-28. [13] Zhang Yujin, Lu Chunming, Biswal BB, et al. Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy[J]. Journal of Biomedical Optics, 2010, 15(4): 047003-047003-8. [14] Zhang Han, Duan Lian, Zhang Yujing, et al. Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy[J]. Neuroimage, 2011, 55(2): 607-615. [15] Zhang Han, Zhang Yujing, Duan Lian, et al. Is resting-state functional connectivity revealed by functional near-infrared spectroscopy test-retest reliable?[J]. Journal of Biomedical Optics, 2011, 16(6): 067008-1-067008-8. [16] Ehlis AC, Schneider S, Dresler T, et al. Application of functional near-infrared spectroscopy in psychiatry[J]. Neuroimage, 2014, 85: 478-488. [17] Liu Xiaomin, Sun Gaoxiang, Zhang Xiaoqian, et al. Relationship between the prefrontal function and the severity of the emotional symptoms during a verbal fluency task in patients with major depressive disorder: a multi-channel NIRS study[J]. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2014, 54: 114-121. [18] Li Jiangxue, Zhu Huilin, Li Xingge, et al. Spontaneous hemodynamic activity in prefrontal cortex of depression patients assessed with functional near-infrared spectroscopy[C]//Progress in Electromagnetics Research (PIERS). Prague: IEEE, 2015: 1853-1857. [19] Matsuo K, Kato N, Kato T. Decreased cerebral haemodynamic response to cognitive and physiological tasks in mood disorders as shown by near-infrared spectroscopy[J]. Psychological Medicine, 2002, 32(06): 1029-1037. [20] Biswal B, Zerrin Yetkin F, Haughton V M, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar mri[J]. MagneticResonance in Medicine, 1995, 34(4): 537-541. [21] Raichle ME. Two views of brain function[J]. Trends in Cognitive Sciences, 2010, 14(4): 180-190. [22] Uhlhaas PJ, Singer W. Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology[J]. Neuron, 2006, 52(1): 155-168. [23] Anand A, Li Y, Wang Y, et al. Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression[J]. Psychiatry Research: Neuroimaging, 2009, 171(3): 189-198. [24] Greicius MD, Flores BH, Menon V, et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus[J]. Biological Psychiatry, 2007, 62(5): 429-437. [25] Sheline YI, Price JL, Yan Zhizi, et al. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus[J]. Proceedings of the National Academy of Sciences, 2010, 107(24): 11020-11025. [26] Chai Xiaoqian J, Whitfield-Gabrieli S, Shinn AK, et al. Abnormal medial prefrontal cortex resting-state connectivity in bipolar disorder and schizophrenia[J]. Neuropsychophar-macology, 2011, 36(10): 2009-2017. [27] Lu Chunming, Zhang Yujing, Biswal BB, et al. Use of fNIRS to assess resting state functional connectivity[J]. Journal of Neuroscience Methods, 2010, 186(2): 242-249. [28] Mesquita RC, Franceschini MA, Boas DA. Resting state functional connectivity of the whole head with near-infrared spectroscopy[J]. Biomedical Optics Express, 2010, 1(1): 324-336. [29] White BR, Snyder AZ, Cohen AL, et al. Resting-state functional connectivity in the human brain revealed with diffuse optical tomography[J]. Neuroimage, 2009, 47(1): 148-156. [30] Rosenbaum D, Hagen K, Deppermann S, et al. State-dependent altered connectivity in late-life depression: a functional near-infrared spectroscopy study[J]. Neurobiology of Aging, 2016, 39: 57-68. [31] Ye JC, Tak S, Jang KE, et al. NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy[J]. Neuroimage, 2009, 44(2): 428-447. [32] Kohno S, Miyai I, Seiyama A, et al. Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis[J]. Journal of Biomedical Optics, 2007, 12(6): 062111-1-062111-9. [33] Zhang Han, Zhang Yujing, Lu Chunming, et al. Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements[J]. Neuroimage, 2010, 51(3): 1150-1161. [34] Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1995, 57: 289-300. [35] Naseer N, Hong K-S. fNIRS-based brain-computer interfaces: A review[J]. Frontiers in Human Neuroscience, 2015, 9: 3. [36] Naseer N, Hong M J, Hong KS. Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface[J]. Experimental Brain Research, 2014, 232(2): 555-564. [37] Koenigs M, Grafman J. The functional neuroanatomy of depression: Distinct roles for ventromedial and dorsolateral prefrontal cortex[J]. Behavioural Brain Research, 2009, 201(2): 239-243.