Analysis of vascular dysregulation caused by infiltrating glioma cells using bold fMRI
Article No.: 18, Pages 1 - 7
Abstract
Malignant glioma is a brain malignancy that can infiltrate into surrounding tissues causing disruption in cerebral blood flow. It is important to identify the regions of vascular dysregulation that may aid to detect the tumor spread. Also, the strength of functional connectivity within the tumor has prognostic value. The purpose of this study was to identify the vascular dysfunction caused by glioma with the help of blood oxygen level-dependent (BOLD) functional MRI (fMRI). Multiple linear regression was performed to identify the regions correlated with tumor cells. Functionally intact voxels within the tumor and the presence of Gaussian noise in BOLD fMRI images were the challenges faced to find an efficient representation for regressors. To address these challenges, we found a better representation for regressors using regional homogeneity maps derived from the tumor and control regions and was tested on images contaminated with Gaussian noise. The proposed method resulted in an improved D prime value of 2.3 which indicates the reliability of our method when compared with the state-of-the-art method.
References
[1]
Hannelore Aerts and Daniele Marinazzo. 2018. BTC_preop.
[2]
Rasmus M Birn, Kevin Murphy, and Peter A Bandettini. 2008. The effect of respiration variations on independent component analysis results of resting state functional connectivity. Human brain mapping 29, 7 (2008), 740--750.
[3]
Stephen G Bowden, Brian JA Gill, Zachary K Englander, Craig I Horenstein, George Zanazzi, Peter D Chang, Jorge Samanamud, A Lignelli, Jeffrey N Bruce, Peter Canoll, et al. 2018. Local glioma cells are associated with vascular dysregulation. American Journal of Neuroradiology 39, 3 (2018), 507--514.
[4]
GB Bradac, RS Simon, and CH Heidsieck. 1976. Angiographically verified transient alteration of the intracranial arteries and veins in dependence on different CO 2 tensions. Neuroradiology 10, 5 (1976), 257--262.
[5]
S Cha, L Yang, G Johnson, A Lai, M-H Chen, T Tihan, M Wendland, and WP Dillon. 2006. Comparison of microvascular permeability measurements, Ktrans, determined with conventional steady-state T1-weighted and first-pass T2*-weighted MR imaging methods in gliomas and meningiomas. American journal of neuroradiology 27, 2 (2006), 409--417.
[6]
Daniel S Chow, Craig I Horenstein, Peter Canoll, Angela Lignelli, Elizabeth MC Hillman, Christopher G Filippi, and Jack Grinband. 2016. Glioblastoma induces vascular dysregulation in nonenhancing peritumoral regions in humans. American Journal of Roentgenology 206, 5 (2016), 1073--1081.
[7]
Robert W Cox. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research 29, 3 (1996), 162--173.
[8]
Robert W Cox and James S Hyde. 1997. Software tools for analysis and visualization of fMRI data. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo 10, 4-5 (1997), 171--178.
[9]
Andy GS Daniel, Ki Yun Park, Jarod L Roland, Donna Dierker, James Gross, Joseph B Humphries, Carl D Hacker, Abraham Z Snyder, Joshua S Shimony, and Eric C Leuthardt. 2021. Functional connectivity within glioblastoma impacts overall survival. Neuro-oncology 23, 3 (2021), 412--421.
[10]
Gaurav Garg, Girijesh Prasad, and Damien Coyle. 2013. Gaussian Mixture Model-based noise reduction in resting state fMRI data. Journal of neuroscience methods 215, 1 (2013), 71--77.
[11]
Rakshatha P Krishnamurthy, Neelam Sinha, Jitender Saini, and Pramod Kumar Pal. 2014. Classification of essential tremors (ET) disorder and healthy controls using a masking technique. In Medical Imaging 2014: Image Processing, Vol. 9034. International Society for Optics and Photonics, SPIE, San Diego, California, United States, 90341Z.
[12]
Megan H Lee, Christopher D Smyser, and Joshua S Shimony. 2013. Restingstate fMRI: a review of methods and clinical applications. American Journal of neuroradiology 34, 10 (2013), 1866--1872.
[13]
Haihong Liu, Zhening Liu, Meng Liang, Yihui Hao, Lihua Tan, Fan Kuang, Yanhong Yi, Lin Xu, and Tianzi Jiang. 2006. Decreased regional homogeneity in schizophrenia: a resting state functional magnetic resonance imaging study. Neuroreport 17, 1 (2006), 19--22.
[14]
Yuri Persidsky, Servio H Ramirez, James Haorah, and Georgette D Kanmogne. 2006. Blood-brain barrier: structural components and function under physiologic and pathologic conditions. Journal of Neuroimmune Pharmacology 1, 3 (2006), 223--236.
[15]
Peter Schramm, Argyro Xyda, Ernst Klotz, Volker Tronnier, Michael Knauth, and Marius Hartmann. 2010. Dynamic CT perfusion imaging of intra-axial brain tumours: differentiation of high-grade gliomas from primary CNS lymphomas. European radiology 20, 10 (2010), 2482--2490.
[16]
Karin Shmueli, Peter van Gelderen, Jacco A de Zwart, Silvina G Horovitz, Masaki Fukunaga, J Martijn Jansma, and Jeff H Duyn. 2007. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. Neuroimage 38, 2 (2007), 306--320.
[17]
Adrian J Simpson and Mike J Fitter. 1973. What is the best index of detectability? Psychological Bulletin 80, 6 (1973), 481.
[18]
Stacey Watkins, Stefanie Robel, Ian F Kimbrough, Stephanie M Robert, Graham Ellis-Davies, and Harald Sontheimer. 2014. Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells. Nature communications 5, 1 (2014), 1--15.
[19]
Xue Yang, Hakmook Kang, Allen T Newton, and Bennett A Landman. 2013. Evaluation of statistical inference on empirical resting state fMRI. IEEE Transactions on Biomedical Engineering 61, 4 (2013), 1091--1099.
[20]
Yufeng Zang, Tianzi Jiang, Yingli Lu, Yong He, and Lixia Tian. 2004. Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 1 (2004), 394--400.
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- Analysis of vascular dysregulation caused by infiltrating glioma cells using bold fMRI
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Published In
December 2021
428 pages
ISBN:9781450375962
DOI:10.1145/3490035
- General Chairs:
- Rama Chellappa,
- Santanu Chaudhury,
- Program Chairs:
- Chetan Arora,
- Parag Chaudhuri,
- Subhransu Maji
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Association for Computing Machinery
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Published: 19 December 2021
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ICVGIP '21
ICVGIP '21: Indian Conference on Computer Vision, Graphics and Image Processing
December 19 - 22, 2021
Jodhpur, India
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Overall Acceptance Rate 95 of 286 submissions, 33%
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