# Computational and Mathematical Methods for Neuroscience
## 3 Highlights and Key Contributions of Published Articles
### Medical Applications  
Half of the papers in this Special Issue (8 out of 16) focus on medical applications, with 4 specifically addressing Alzheimer's disease (AD) (contributions 2, 9, 11, and 14). This focus is aligned with the critical importance of early and accurate AD diagnosis, which enables timely therapeutic intervention and management. Brain imaging technologies like MRI and PET scans facilitate the early detection of AD-related structural and functional changes in the brain, often identifying the disease before severe clinical symptoms appear. Early detection provides valuable opportunities for intervention that may slow disease progression. Additionally, brain imaging distinguishes AD from other dementias, such as Lewy body or vascular dementia, by detecting unique patterns like amyloid plaques or hippocampal atrophy. Moreover, imaging allows clinicians to track disease progression over time, informing treatment adjustments and helping to assess therapeutic efficacy. Among available neuroimaging techniques, MRI is particularly popular in AD research, as it visualizes brain structures, allowing clinicians to detect hippocampal atrophy, a key marker of AD. In this issue, [PERSON] et al. (**contribution 2**) introduce an innovative deep learning approach to automatically diagnose AD using MRI datasets. Leveraging the strengths of deep learning, which often outperforms human detection in assessing AD stages, the authors employ pre-trained convolutional neural networks (CNNs) to classify AD severity with high accuracy, even when dataset quality and quantity are limited. Their method, which preprocesses AD data through an advanced image processing module before training, achieves a 99.3% accuracy rate--an improvement over existing models. [PERSON] and [PERSON] (**contribution 9**) review studies on tacrine and its derivatives labeled with radionuclides, exploring their potential as diagnostic radiotracers for AD. While AD is not curable, its progression and symptoms can be managed using treatments like acetylcholinesteraseonitesterase (AChE) inhibitors (e.g., tacrine, rivastigmine, galantamine, and donepezil). The authors analyze radiolabeled tacrine derivatives in early AD diagnosis, with a particular emphasis on computational molecular modeling to visualize tacrine's interaction with cholinesterase. Their review highlights the limitations of current radiopharmaceuticals based on tacrine derivatives and suggests a shift toward other biomolecules relevant to early AD stages. [PERSON] (**contribution 11**) presents a novel integrated model combining LeViT, EfficientNet B7, and Dartooster XGBoost (DXB) for AD detection using MRI. LeViT is a vision transformer-based hybrid neural network, EfficientNet B7 is a high-performance CNN, and DXB is a robust model blending DART and XGBoost algorithms for predictive accuracy. Using MRI datasets totaling 86,390 images, [PERSON]'s approach achieved 99.8% average gen eralization accuracy, underscoring the potential of multi-model fusion for high-precision AD detection. [PERSON] et al. (**contribution 14**) examine brain-wide structural connectomics in early AD stages. Analyzing a longitudinal diffusion-weighted imaging dataset of 264 subjects, they apply a tailored machine learning approach that combines exhaustive tractography with neuropsychological data to achieve high classification accuracy. Their model identifies early biomarkers of AD based on hemispheric lateralization of mean tract volume for specific tracts in the supramarginal and paracentral regions, demonstrating the predictive value of diffusion MRI and the importance of multi-modal data integration in neurodegenerative disease research. Other medical applications in this issue cover cerebral palsy (CP), head tremor, sports medicine, and epilepsy (contributions 5, 6, 13, and 16, respectively). [PERSON], [PERSON], and [PERSON] (**contribution 5**) conducted an in-depth EEG study comparing the neural responses of seven patients with cerebral palsy (CP) to a control group of four healthy participants.