Predicting Human Neurotherapeutic Response through Preclinical Imaging Intelligence

In the study and research of neurodegenerative diseases like Alzheimer’s disease (AD), neuroimaging serves as a vital conduit between preclinical assessment, clinical translation and molecular discovery. With the purpose to evaluate therapeutic response and neuroprotective efficacy this work offers an integrative framework that combines artificial intelligence (AI), nanomedicine and neuroimaging. At the preclinical stage, in order to improve brain penetration and lessen neurotoxicity, Ursolic acid (UA) based nano formulations are created and evaluated for their physicochemical and pharmacokinetic benefits. Advanced imaging modalities such as MRI and fluorescence/confocal microscopy are used to visualize the distribution of nanoparticles, the permeability of the blood-brain barrier and the reduction of neuroinflammation in the respective rodent models. In order to correlate with histopathological findings and behavioral outcomes, quantitative imaging biomarkers such as volumetric changes, cortical thickness and diffusion parameters are extracted. Cerebrospinal fluid (CSF) biomarkers (Aβ tau neurofilament light chain) and regional brain changes are correlated with cognitive performance and disease progression using multimodal imaging datasets (MRI, PET and connectomic analyses) at the clinical level. With the aim to align preclinical and clinical imaging representations, an AI-driven, domainadapted, 3D convolutional neural network (CNN) is used. The model combines segmentation, feature encoding and domain adaptation to forecast human biomarker trends based on preclinical imaging responses. Validation highlights the translational potential of computational modeling by evaluating the predictive correlation between AI-derived features and human CSF/cognitive metrics, bridging laboratory findings and clinical neuroimaging, thus, accelerating predictive evaluation of nanomedicines prior to human trials.

Alzheimer’s disease, Neuroimaging, Ursolic acid, Nanomedicine, Artificial Intelligence, Preclinical models, Translational neuroscience

1. Kaushik, A., Kaushik, A., Mor, R., Kaura, S., & Sharma, S. (2023). Synthesis and characterization of Gum Acacia encapsulated Ursolic acid nanoparticles enhancing bioavailability and acetylcholinesterase inhibition for therapeutic approach of Alzheimer’s disease. African Journal of Biological Sciences, 5(3), 156-169.https://doi.org/10.48047/AFJBS.5.3.2023.156-169 [Google Scholar] [Crossref]

2. Kaushik, A., Kaura, S., & Mor, R. (2025). Synthesis and characterization of Pullulan encapsulated Ursolic acid nanoparticles for enhanced bioavailability and acetylcholinesterase inhibition in Alzheimer’s disease therapy. International Journal of Pharmacy Research & Technology (IJPRT), 15(1), 124-139. https://ijprt.org/index.php/pub/article/view/334https://ijprt.org/index.php/pub/article/view/334 [Google Scholar] [Crossref]

3. Kaushik, A., Mor, R., & Kaura, S. (2025). The potential of Ursolic acid nanoformulations as drug delivery systems in Alzheimer’s disease therapy and research. International Journal of Latest Technology in Engineering Management & Applied Science, 14(4), 795-800. https://doi.org/ 10.51583/ IJLTEMAS .2025 .140400094 [Google Scholar] [Crossref]

4. Kaushik, A., Mor, R., Kaushik, A., & Kaura, S. (2025). Redefining preclinical neuroscience: AI-driven in-silico models as ethical and efficient alternatives to animal testing in Alzheimer’s nanomedicine research. International Journal of Research and Scientific Innovation (IJRSI), 12(15), 2003-2016. Special Issue on Public Health. https://doi.org/10.51244/IJRSI.2025.1215000154P [Google Scholar] [Crossref]

5. Lan, H., Varghese, B. A., Sheikh-Bahaei, N., Sepehrband, F., Toga, A. W., & Choupan, J. (2025). Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details. NeuroImage, 316, 121297. https://doi.org/10.1016/j.neuroimage.2025.121297 [Google Scholar] [Crossref]

6. Dinsdale, N. K., Jenkinson, M., & Namburete, A. I. L. (2023). SFHarmony: Source-Free Domain Adaptation for Distributed Neuroimaging Analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 11494-11505). https://doi.org/ 10.1109/ICCV 51070 .2023.01056 [Google Scholar] [Crossref]

7. He, S., Guan, Y., Cheng, C.H., Moore, T.L., Luebke, J.I., Killiany, R.J., Rosene, D.L., Koo, B.-B. & Ou, Y. (2023). Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. Frontiers in Aging Neuroscience, 15, 1249415. https://doi.org/ 10.3389/ fnagi.2023.1249415 [Google Scholar] [Crossref]

8. Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., & Colliot, O. (2020). Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Medical Image Analysis, 63, 101694. https:// doi.org/10.1016/j.media.2020.101694 [Google Scholar] [Crossref]

9. Munroe, L., da Silva, M., Heidari, F., Grigorescu, I., Dahan, S., Robinson, E. C., Deprez, M., & So, P.W. (2024). Applications of interpretable deep learning in neuroimaging: A comprehensive review. Imaging Neuroscience, 2, 1-37. https://doi.org/10.1162/imag_a_00214 [Google Scholar] [Crossref]

10. Hussain, M. Z., Shahzad, T., Mehmood, S., et al. (2025). A fine-tuned convolutional neural network model for accurate Alzheimer’s disease classification. Scientific Reports, 15, 11616. https://doi.org/ 10.1038/ s41598 -025-86635-2 [Google Scholar] [Crossref]

11. Sicilia, A., Zhao, X., & Hwang, S. J. (2023). Domain adversarial neural networks for domain generalization: When it works and how to improve. Machine Learning, 112(7), 2685–2721. https:// doi.org/10.1007/s10994-023-06324-x [Google Scholar] [Crossref]

12. Yousefnezhad, M., Zhang, D., Greenshaw, A. J., & Greiner, R. (2022). Editorial: Multi-site neuroimage analysis: Domain adaptation and batch effects. Frontiers in Neuroinformatics, 16, 994463. https:// doi.org/10.3389/fninf.2022.994463 [Google Scholar] [Crossref]

• Problem-Based Learning Strategy: Effect on Achievement of Genetics among Biology Students in Colleges of Education Oyo, Oyo State
• Gut Content Analysis of Siganus canaliculatus (Danggit)
• Can Algicidal Bacteria be the Solution to Successfully Manage Freshwater Algal Blooms?
• Boosting Carbon Capture: Progress in Enhancing RuBisCO’s Carboxylase Activity
• Strategies and Effective Learning in Genetics and Genomics Courses for Undergraduate Students: A Systematic Literature Review