Citation:

Simrith Pulicharla, 2025. "Creating Real-Time Intraoperative Brain Mapping Tools That Don’t Disrupt Surgery", ESP Journal of Engineering & Technology Advancements  5(2): 90-98.

Abstract:

Intraoperative brain mapping plays a pivotal role in modern neurosurgery, ensuring that critical brain functions are preserved while minimizing the risk of permanent damage. Current techniques, such as electrocortical stimulation, functional MRI, intraoperative neurophysiological monitoring, and near-infrared spectroscopy, offer valuable insights into brain activity during surgery. However, each method has its limitations, ranging from invasiveness and limited spatial resolution to the inability to monitor deep brain structures or provide real-time feedback. As the need for more precise, efficient, and less invasive mapping solutions grows, the development of new technologies and approaches becomes imperative. The next steps in neurosurgical innovation must focus on overcoming these limitations, offering real-time, comprehensive brain mapping tools that can guide surgeons with greater accuracy and ultimately improve patient outcomes.

References:

[1] Duffau, H. (2006). Brain mapping in tumor surgery: functional anatomy, neuroplasticity and surgical outcomes. Nature Reviews Neurology, 2(8), 476–486. https://doi.org/10.1038/nrneurol.2006.106

[2] Pulicharla, Mohan Raja. "Hybrid quantum-classical machine learning models: powering the future of AI." Journal of Science & Technology 4.1 (2023): 40-65.

[3] Berger, M. S., & Hadjipanayis, C. G. (2007). Functional mapping-guided resection of low-grade gliomas in eloquent areas of the brain. Journal of Neurosurgery, 106(2), 268–274. https://doi.org/10.3171/jns.2007.106.2.268

[4] Pulicharla, Mohan Raja. "Data Versioning and Its Impact on Machine Learning Models." Journal of Science & Technology 5.1 (2024): 22-37.

[5] Li, F., Liu, M., Dai, Y., et al. (2020). Deep learning-based real-time detection of functional brain areas using ECoG. Frontiers in Neuroscience, 14, 399. https://doi.org/10.3389/fnins.2020.00399

[6] Pulicharla, Mohan Raja. "A Study On a Machine Learning Based Classification Approach in Identifying Heart Disease Within E-Healthcare." J Cardiol & Cardiovasc Ther 19.1 (2023): 556004.

[7] Miller, K. J., Zanos, S., Fetz, E. E., et al. (2009). Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. The Journal of Neuroscience, 29(10), 3132–3137. https://doi.org/10.1523/JNEUROSCI.5506-08.2009

[8] Soloukey, S., et al. (2020). Functional Ultrasound (fUS) during awake brain surgery: The beginning of a new era in functional imaging. Frontiers in Neuroscience, 13, 1384. https://doi.org/10.3389/fnins.2019.01384

[9] Mohan Raja Pulicharla, Dr. Y. V. Rao 2023. Neuro-Evolutionary Approaches for Explainable AI (XAI). Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal. 12, 1 (Jun. 2023), 334–341.

[10] Mace, E., et al. (2011). Functional ultrasound imaging of the brain. Nature Methods, 8(8), 662–664. https://doi.org/10.1038/nmeth.1641

[11] Liu, Y., et al. (2015). Soft and elastic hydrogel-based electronics for localized low-voltage neuromodulation. Nature Biomedical Engineering, 1(7), 1–12. https://doi.org/10.1038/s41551-017-0135-2

[12] Xie, C., et al. (2015). Nanoelectronic coatings for minimally invasive neural interfaces. Science Advances, 1(9), e1500732.https://doi.org/10.1126/sciadv.1500732

Keywords:

Real-Time Brain Mapping, Intraoperative Brain Mapping, Neurosurgical Tools, Functional Brain Mapping, Non-Disruptive Neurosurgery, Surgical Navigation, Brain-Computer Interface, Electrophysiological Monitoring, Electrocorticography (Ecog), Functional MRI (Fmri), Neural Signal Processing, Neuroimaging Techniques, Cortical Mapping, Optical Imaging, Augmented Reality Surgery, Intraoperative Neurophysiology, Minimally Invasive, Surgical Precision, Patient Safety, Neurosurgical Workflow, Motor Cortex Mapping, Language Area Localization, Real-Time Feedback, Brain Function Preservation.