Functional and Structural Alterations in Temporal Lobe Tumor Patients: Evidence of Functional Compensation in the Human Brain

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Abstract Patients with temporal lobe tumor are frequently accompanied by cognitive impairments. This study aims to combine diffusion tensor imaging (DTI) with resting-state functional magnetic resonance image (fMRI) to explore the underlying cognitive alternation and functional reorganization in patients with temporal lobe tumor. Twenty patients with right temporal lobe tumor and seventeen healthy volunteers were recruited in this study. Independent component analysis (ICA) was utilized to divide the whole brain into six sub functional networks, and DTI combined with graph-based topological analysis was used to calculate the structural properties of whole-brain and sub functional networks. Besides, the rich hubs in both patients and healthy controls (HCs) were identified and a further analysis of their internal functional and structural connectivity was also conducted. The E_global in self-referential network (SRN) of patients is higher than HCs, while decreased E_global of dorsal attention network (DAN) and default mode network (DMN) were found in patients. For the patients, brain regions with increased nodal efficiency(E_nodal) were located in the frontal lobe (corresponding to SRN). Six identical hubs were found in two groups. Compared patients with HCs, significant between-group differences in both functional and structural connectivity were observed between right insula (INS.R) and right lenticular nucleus, putamen (PUT.R). The findings provide evidence that impaired cognition are closely related to functional and structural abnormalities and the compensatory roles of frontal lobe were also identified in patients. The proposed combination of DTI and fMRI promises a widespread utilization in clinical research on the mechanisms of neurological diseases.

Key words： temporal lobe tumor resting state fMRI diffusion tensor imaging compensatory mechanism

Received: 05 June 2018

Fund:National Natural Science Foundation of China; grant number: 61275199, 61378092 and 81601532; grant sponsor: the Fundamental Research Funds for the Central Universities; grant number: NS2015032, NP2015201; grant sponsor: Natural Science Foundation of Jiangsu Province; grant number: BK20160814; grant sponsor: Scientific Research Foundation of Nanjing University of Aeronautics and Astronautics; number: 1003-YAH16009

Corresponding Authors: XUE Li. E-mail: 15950522355@163.com

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	XUE Li
	YANG Yu-xuan
	LING Tao
	YANG Ya-min
	QIAN Zhi-yu

Cite this article:

XUE Li,YANG Yu-xuan,LING Tao, et al. Functional and Structural Alterations in Temporal Lobe Tumor Patients: Evidence of Functional Compensation in the Human Brain[J]. Chinese Journal of Biomedical Engineering, 2018, 27(3): 109-125.

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http://manu32.magtech.com.cn/Jwk_zgswyx_en/EN/ OR http://manu32.magtech.com.cn/Jwk_zgswyx_en/EN/Y2018/V27/I3/109

[1] Greicius MD, Krasnow B, Reiss AL, et al.Functional connectivity in the resting brain: a network analysis of the default mode hypothesis[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(1):253-258.
[2] Van den Heuvel MP, Mandl RC, Hulshoff Pol HE. Normalized group clustering of resting-state fMRI data[J]. PLoS ONE, 2008: e2001
[3] Zhu WZ, Tao L, Qian ZY, et al.Analysis of brain functional network of glioma patients based on white matter fiber tracking[J]. Beijing Biomedical Engineering, 2013, 32(5): 449-455.
[4] Yoshii Y, Tominaga D, Sugimoto K, et al.Cognitive function of patients with brain tumor in pre- and postoperative stage[J]. Surgical Neurology, 2008, 69(1):51-61.
[5] Chenevert TL, Brunberg JA, Pipe JG.Anisotropic diffusion in human white matter: demonstration with MR techniques in vivo.[J]. Radiology, 1990, 177(2):401-405.
[6] Biswal B, Yetkin FZ, Haughton VM, et al.Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.[J]. Magnetic Resonance in Medicine, 1995, 34(4):537.
[7] Werring D, Clark C, Parker G.A direct demonstration of both structure and function in the visual system: combining diffusion tensor imaging with functional magnetic resonance imaging[J]. Neuroimage, 1999, 361:352-361.
[8] Honey C, Sporns O, Cammoun L, et al.Predicting resting-state functional connectivity from structural connectivity[C]. 14th Annual Meeting Human Brain Mapping, 2008:2035-2040.
[9] Tantillo G, Peck KK, Arevaloperez J, et al.Corpus callosum diffusion and language lateralization in patients with brain tumors: A DTI and fMRI study[J]. Neuroimaging Official Journal of the American Society of Neuroimaging, 2016, 26(2):224.
[10] Gong G, He Y, Concha L, et al.Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography[J]. Cerebral Cortex, 2009, 19(3):524.
[11] Jiang H, van Zijl PC, Kim J, et al. DTI studio: resource program for diffusion tensor computation and fiber bundle tracking[J]. Computer Methods & Programs in Biomedicine, 2006, 81(2):106-116.
[12] Mantini D, Perrucci MG, Del Gratta C, et al.Electrophysiological signatures of resting state networks in the human brain[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(32):13170-13175.
[13] Wang B, Fan Y, Lu M, et al.Brain anatomical networks in world class gymnasts: A DTI tractography study[J]. Neuroimage, 2013, 65: 476-487.
[14] Hagmann P, Cammoun L, Gigandet X, et al.Mapping the structural core of human cerebral cortex[J]. PLoS Biology, 2008, 6: e159.
[15] Yao S, Zeng W, Wang N.An improved ICA method on resting state fMRI data analysis[C]. ICCSEE-13, 2013.
[16] Li YO, Adali T, Calhoun VD.Estimating the number of independent components for functional magnetic resonance imaging data[J]. Human Brain Mapping, 2007, 28(11):1251.
[17] Raichle, ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function[J]. Proceedings of the National Academy of Sciences of the United States of America, 2001, 98(2): 676-682.
[18] Buckner RL.The brain's default network: origins and implications for the study of psychosis[J]. Dialogues in Clinical Neuroscience, 2013, 15(3):351.
[19] Heimans JJ, Reijneveld JC, Stam CJ.Magnetoencephalography, functional connectivity, and neural network topology in diffuse low-grade gliomas[M]. Diffuse Low-Grade Gliomas in Adults. Springer London, 2013:321-336.
[20] De JA, de Munck JC, Baayen JC, et al. Localization of fast MEG waves in patients with brain tumors and epilepsy[J]. Brain Topography, 2003, 15(3):173-179.
[21] Su TW, Hsu TW, Lin YC, et al.Schizophrenia symptoms and brain network efficiency: A resting-state fMRI study[J]. Psychiatry Research Neuroimaging, 2015, 234(2):208-218.
[22] Jin C, Lin L, Zhang BW, et al.The comparison of hub identification methods in DTI brain network[J]. Smart Healthcare, 2016, 2(4): 1-6.
[23] Takahashi T, Malhi GS, Wood SJ, et al.Gray matter reduction of the superior temporal gyrus in patients with established bipolar I disorder[J]. Journal of Affective Disorders, 2010, 123(1-3):276-282.
[24] Ellison A, Schindler I, Pattison LL, et al.An exploration of the role of the superior temporal gyrus in visual search and spatial perception using TMS[J]. Brain, 2004, 127(10):2307-2315.
[25] Leff AP, Schofield TM, Crinion JT, et al.The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke[J]. Brain, 2009, 132(12):3401-3410.
[26] Kong F, Hu S, Wang X, et al.Neural correlates of the happy life: the amplitude of spontaneous low frequency fluctuations predicts subjective well-being[J]. Neuroimage, 2015, 107:136-145.
[27] Lan CC, Shih-Jen T, Huang CC, et al.Functional connectivity density mapping of depressive symptoms and loneliness in non-demented elderly male[J]. Frontiers in Aging Neuroscience, 2015, 7(7):251.
[28] Kukolja J, Greci DY, Onur A, et al.Resting-state fMRI evidence for early episodic memory consolidation: effects of age[J]. Neurobiology of Aging, 2016, 45:197-211.
[29] Rosiene J, Liu X, Imielinska C, et al.Structure-function relationships in the human visual system using DTI, fMRI and visual field testing: pre- and post-operative assessments in patients with anterior visual pathway compression[J]. Studies in Health Technology & Informatics, 2006, 119:464.
[30] Vaessen MJ, Hofman PA, Tijssen HN, et al.The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures[J]. Neuroimage, 2010, 51(3):1106-1116.
[31] Bassett DS, Brown JA, Deshpande V, et al.Conserved and variable architecture of human white matter connectivity[J]. Neuroimage, 2011, 54(2):1262-1279.
[32] Iturria-Medina Y, Sotero RC, Canales-Rodríguez EJ, et al.Studying the human brain anatomical network via diffusion-weighted MRI and graph theory[J]. Neuroimage, 2008, 40(3):1064-1076.