Brain Sciences | Highly Cited Papers in 2024 in the “Computational Neuroscience, Neuroinformatics, and Neurocomputing” Section
The “Computational Neuroscience, Neuroinformatics, and Neurocomputing” Section of Brain Sciences (ISSN: 2076-3425) aim to improve our understanding of brain function, which requires interdisciplinary collaborations between theoretical, computational, and experimental disciplines and approaches. This Section fosters multidisciplinary interactions between theoretical, computational, and experimental work in the field of neuroscience.
This Section invites original contributions on a wide range of topics that promote theoretical modeling focused on understanding neural function at the molecular, cellular, and circuit levels via computational and model-based approaches that are experimentally testable. While the Section is primarily focused on theoretical and computational research, it welcomes experimental studies that validate and test theoretical conclusions. Primarily theoretical manuscripts should be highly relevant to the neural mechanisms of the neural function, while primarily experimental manuscripts should have implications for the computational analysis of nervous system function.
Manuscripts investigating physiological mechanisms underlying neuropathologies by combining theoretical and experimental approaches are highly encouraged. Similarly, manuscripts describing novel technological advances in data analysis techniques to further insights into the function of the nervous system are also highly encouraged. Modeling approaches at all levels, from biophysically motivated realistic simulations of neurons and synapses to high-level behavioral models of inference and decision-making, are also welcome.
All articles published in our journal are available in an open access format, granting you free, unrestricted access to the full text. We invite you to explore our most highly cited papers in 2024, listed below:
1. “Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning”
by Xiuzhen Yao, Tianwen Li, Peng Ding, Fan Wang, Lei Zhao, Anmin Gong, Wenya Nan and Yunfa Fu
Brain Sci. 2024, 14(3), 268; https://doi.org/10.3390/brainsci14030268
Available online: https://www.mdpi.com/2076-3425/14/3/268
2. “Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions”
by Hamed Mohammadi and Waldemar Karwowski
Brain Sci. 2025, 15(1), 17; https://doi.org/10.3390/brainsci15010017
Available online: https://www.mdpi.com/2076-3425/15/1/17
3. “A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults”
by Utkarsh Lal, Arjun Vinayak Chikkankod and Luca Longo
Brain Sci. 2024, 14(4), 335; https://doi.org/10.3390/brainsci14040335
Available online: https://www.mdpi.com/2076-3425/14/4/335
4. “Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction”
by Jinhao Zhang, Yanrong Hao, Xin Wen, Chenchen Zhang, Haojie Deng, Juanjuan Zhao and Rui Cao
Brain Sci. 2024, 14(3), 271; https://doi.org/10.3390/brainsci14030271
Available online: https://www.mdpi.com/2076-3425/14/3/271
5. “EEG-Based Seizure Prediction Using Hybrid DenseNet–ViT Network with Attention Fusion”
by Shasha Yuan, Kuiting Yan, Shihan Wang, Jin-Xing Liu and Juan Wang
Brain Sci. 2024, 14(8), 839; https://doi.org/10.3390/brainsci14080839
Available online: https://www.mdpi.com/2076-3425/14/8/839
6. “The Clinical Relevance of Artificial Intelligence in Migraine”
by Angelo Torrente, Simona Maccora, Francesco Prinzi, Paolo Alonge, Laura Pilati, Antonino Lupica, Vincenzo Di Stefano, Cecilia Camarda, Salvatore Vitabile and Filippo Brighina
Brain Sci. 2024, 14(1), 85; https://doi.org/10.3390/brainsci14010085
Available online: https://www.mdpi.com/2076-3425/14/1/85
7. “Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies”
by Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan and Alireza Tavakkoli
Brain Sci. 2024, 14(12), 1266; https://doi.org/10.3390/brainsci14121266
Available online: https://www.mdpi.com/2076-3425/14/12/1266
8. “Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study”
by Miloš Pušica, Aneta Kartali, Luka Bojović, Ivan Gligorijević, Jelena Jovanović, Maria Chiara Leva and Bogdan Mijović
Brain Sci. 2024, 14(2), 149; https://doi.org/10.3390/brainsci14020149
Available online: https://www.mdpi.com/2076-3425/14/2/149
9. “Review of EEG Affective Recognition with a Neuroscience Perspective”
by Rosary Yuting Lim, Wai-Cheong Lincoln Lew and Kai Keng Ang
Brain Sci. 2024, 14(4), 364; https://doi.org/10.3390/brainsci14040364
Available online: https://www.mdpi.com/2076-3425/14/4/364
10. “The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer’s Disease Using GPT Embeddings”
by Benjamin S. Runde, Ajit Alapati and Nicolas G. Bazan
Brain Sci. 2024, 14(3), 211; https://doi.org/10.3390/brainsci14030211
Available online: https://www.mdpi.com/2076-3425/14/3/211
11. “A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network”
by Xiuli Du, Xiaohui Ding, Meiling Xi, Yana Lv, Shaoming Qiu and Qingli Liu
Brain Sci. 2024, 14(4), 375; https://doi.org/10.3390/brainsci14040375
Available online: https://www.mdpi.com/2076-3425/14/4/375
12. “MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition”
by Yuxiao Du, Han Ding, Min Wu, Feng Chen and Ziman Cai
Brain Sci. 2024, 14(4), 344; https://doi.org/10.3390/brainsci14040344
Available online: https://www.mdpi.com/2076-3425/14/4/344