Abstract 1254P
Background
The Brain OMX diagnostic tool is designed to facilitate the analysis of both structural and functional connectivity of the brain using T1-weighted structural MRI (T1) and resting-state functional magnetic resonance imaging (rs-fMRI) data. This tool utilizes a retrospective approach to gain insights into the intricate workings of the brain and its underlying connections which also assist in various stages of surgical treatment needed in neurodegenerative diseases.
Methods
The Fast Surfer pipeline is designed to be computationally efficient, allowing for the rapid processing of large-scale MRI datasets. T1-weighted structural MRI provides detailed information about the anatomical features of the brain. Functional connectivity is measured by assessing the synchronization of the Blood Oxygenation Level Dependent (BOLD) signals between different brain regions.
Results
Auditory network, cingulate network, Default Mode network, DMN and Occipital network, Dorsal Attention network, Language network, Left attention network, Right Attention network, salience network, and visual network correlations are provided. Brain complexity, neurological disorders, and personalized treatment strategies can be achieved. The correlation matrix creates a comprehensive graph from RS-FMRI.
Table: 1254P
Structural analysis using BrainOMX
Subregion | Total | Left | Right | |||
Volume [cm3] | % ICV | Volume [cm3] | % ICV | Volume[cm3] | % ICV | |
Cortical white matter | 462.49 | 33.77% | 230.76 | 16.85 | 231.73 | 16.92 |
Thalamus | 13.97 | 1.02% | 7.11 | 0.52 | 6.85 | 0.50 |
Caudate | 6.13 | 0.45% | 2.96 | 0.22 | 3.17 | 0.23 |
Putamen | 9.46 | 0.69% | 4.72 | 0.34 | 4.74 | 0.35 |
Pallidum | 3.67 | 0.27% | 1.87 | 0.14 | 1.80 | 0.13 |
Hippocampus | 7.18 | 0.52% | 3.50 | 0.26 | 3.68 | 0.27 |
Amygdala | 2.71 | 0.20% | 1.36 | 0.10 | 1.35 | 0.10 |
Conclusions
By combining both structural and functional connectome analyses, the Brain OMX diagnostic tool aims to provide a comprehensive understanding of the brain's architecture for carcinomas and also its implications for neurological conditions. The tabulation shows Alzheimers neurodegenrative patient data, he connectivity can be the result of a direct anatomic connection or an indirect path via a mediating region or may have no known anatomic correlate. rs-fMRI is an imaging technique that plays a growing role in characterizing normal and abnormal functional brain connectivity in a variety of clinical conditions.
Clinical trial identification
Editorial acknowledgement
Data Acknowledgement:
1. Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M, FastSurfer - A fast and accurate deep learning based neuroimaging pipeline, NeuroImage 219 (2020), 117012. https://doi.org/10.1016/j.neuroimage.2020.117012
2. Henschel L*, Kügler D*, Reuter M. (*co-first). FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods - A Solution for HighRes Brain MRI. NeuroImage 251 (2022), 118933. \\http://dx.doi.org/10.1016/j.neuroimage.2022.118933.
3. Zolotova, S. V., Golanov, A. V., Pronin, I. N., Dalechina, A. V., Nikolaeva, A. A., Belyashova, A. S., Usachev, D. Y., Kondrateva, E. A., Druzhinina, P. V., Shirokikh, B. N., Saparov, T. N., Belyaev, M. G., & Kurmukov, A. I. (2023). Burdenko’s Glioblastoma Progression Dataset (Burdenko-GBM-Progression) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/E1QP-D183
4. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. \\https://doi.org/10.1007/s10278-013-9622-7
5. Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L., & Quarles, C. C. (2020). GBM-DSC-MRI-DRO [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2020.RMWVZWIX
6. Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L., & Quarles, C. C. (2017). A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. In Tomography (Vol. 3, Issue 1, pp. 41–49). MDPI AG. \\https://doi.org/10.18383/j.tom.2016.00286
7. Kathleen M Schmainda, Melissa A Prah, Jennifer M Connelly, Scott D Rand. (2016). Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs [ Dataset ]. The Cancer Imaging Archive. \\DOI: 10.7937/K9/TCIA.2016.5DI84Js8
8. Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC. (2018). Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. American Journal of Neuroradiology, 39(6), 1008–1016. DOI: 10.3174/ajnr.a5675
9.Barboriak, D. (2015). Data From RIDER NEURO MRI. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.VOSN3HN1.
10. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. \\https://doi.org/10.1007/s10278-013-9622-7 PMCID: PMC3824915
Legal entity responsible for the study
PMX Inc., USA, and PMX, Seoul, Republic of Korea.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
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