NATALIA ZAKHAROVA |  Brain tractography data  | Best Researcher Award

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Assist. Prof. Dr. NATALIA ZAKHAROVA |  Brain tractography data  | Best Researcher Award

Leading Researcher at The Institute of Personalized Psychiatry and Neurology, St Petersburg V. M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Russia

Dr. Natalia Zakharova is a highly accomplished psychiatrist and neuroscientist with extensive clinical and academic experience. She currently serves as the Head of the Laboratory for Fundamental Research Methods at the Center of Neuropsychiatry, Mental-Health Clinic No. 1 named after N.A. Alexeev, under the Moscow Healthcare Department. Her work integrates psychiatric practice with innovative approaches in clinical research, genetics, and neuropsychiatric treatment.

🔹Professional Profile:

Scopus

Orcid

🎓Education Background

Dr. Zakharova earned her M.D. from Volgograd State Medical University in 2004 and completed a clinical internship in psychiatry at the Sechenov Moscow Medical Academy in 2005. She pursued postgraduate studies at the Research Center for Mental Health and defended her Ph.D. dissertation in 2015 on “Remissions in recurrent depressive disorder (epidemiology, typological differentiation, therapy).”

💼 Professional Development

Dr. Zakharova has held multiple leadership and research roles across prestigious institutions. Since 2021, she has led the Laboratory for Fundamental Research Methods at the Mental-Health Clinic No. 1. From 2017 to 2021, she was a senior researcher at the Education Center of the same clinic. Earlier, she served as an assistant professor in psychiatry and medical psychology at the Russian National Research Medical University and as a researcher at the Scientific Center for Mental Health. In parallel, she acted as a scientific consultant for the xGenCloud project and coordinated the “PsyGenCheck” program. Clinically, she has worked as a practicing psychiatrist since 2005, including in emergency psychiatric services.

🔬Research Focus

Her research focuses on recurrent depressive disorders, clinical genetics in psychiatry, psychosomatic disorders, borderline mental pathology, transcranial magnetic stimulation, and qualitative research methodology. She has contributed to initiatives involving psychiatric genomics and the automation of genetic test interpretation.

📈Author Metrics:

  • RSCI Publications: 90

  • RSCI Citations: 224

  • RSCI Hirsch Index: 7

  • Scopus Publications: 54

  • Scopus Hirsch Index: 8

🏆Awards and Honors:

  • Recognized by the Moscow Department of Health with multiple commendations.

  • Psychiatrist of the highest qualification category.

  • Author of a registered database on depression relapse and remission (No. 2015620683).

  • Holds multiple certifications in advanced psychiatry, clinical research methodology, medical education, and neuromodulation techniques.

📝Publication Top Notes

  • Veǐko, N.N., Ershova, E.S., Kondratyeva, E.I., Zakharova, N.V., Kostyuk, S.V.
    Title: Copy Number Variations of Human Ribosomal Genes in Health and Disease: Role and Causes
    Journal: Frontiers in Bioscience, 2025
    Topic: Genetic mechanisms in neuropsychiatric disorders, ribosomal gene variation
    Citations: 0 (as of now)

  • Zhemchuzhnikov, A.D., Kartashov, S.I., Kozlov, S.O., Mamedova, G.S., Kaydan, M.A.
    Title: The Search for the Most Informative Areas for the Binary Classification of Schizophrenia Using Resting fMRI Data Based on a Method for Extracting Functionally Homogeneous Areas
    Journal: Neuroscience and Behavioral Physiology, 2025
    Topic: Resting-state fMRI biomarkers in schizophrenia
    Citations: 0

  • Zhemchuzhnikov, A.D., Kartashov, S.I., Kozlov, S.O., Mamedova, G.S., Kaydan, M.A.
    Title: On Most Informative Regions for Binary Classification of Schizophrenia Based on Resting State fMRI Data Done by Selection of Functionally Homogeneous Regions Method
    Journal: Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I.P. Pavlova, 2024
    Topic: Computational neuroimaging in schizophrenia classification
    Citations: 0

  • Zakharova, N.V., Bravve, L.V., Mamedova, G.S., Skurinova, M., Oshevsky, D.S.
    Title: Personal Dimensions in First Psychotic Episode Patients with Catatonia
    Type: Conference Paper
    Topic: Personality traits and clinical features in catatonic psychosis
    Citations: 0

  • Bravve, L.V., Mamedova, G.S., Kaydan, M.A., Zaborin, A.S., Zakharova, N.V.
    Title: Magnetic Resonance Imaging in the Study of Catatonia: Use of DWI and Resting State fMRI
    Journal: Psychiatry (Moscow), 2024
    Topic: Neuroimaging techniques in catatonia research
    Citations: 0

Conclusion

Dr. Natalia Zakharova is an outstanding candidate for the Best Researcher Award, exemplifying excellence in interdisciplinary psychiatric research, clinical innovation, and leadership. Her work bridges genomic science, neuroimaging, and psychiatry with practical clinical application, making her contributions both scientifically impactful and clinically meaningful.

Her recognition would not only honor her current achievements but also encourage continued leadership in the future of personalized psychiatry. With modest enhancements in international engagement and outreach, she is poised to become a globally recognized leader in neuropsychiatric research.

➡️ Recommendation: Strongly recommended for the award.

Shurun Wang | Brain function connectivity analysis | Best Researcher Award

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Dr. Shurun Wang | Brain function connectivity analysis | Best Researcher Award

Postdoctoral researcher at  University of Science and Technology, China📝

Dr. Shurun Wang is a postdoctoral researcher at the School of Information Science and Technology, University of Science and Technology of China (USTC), specializing in biomedical signal analysis and brain function connectivity analysis. He holds a Ph.D. from the School of Electrical Engineering and Automation, Hefei University of Technology, where he also earned his MSc and BSc degrees. Dr. Wang has actively contributed to academic research and is passionate about advancing understanding in brain connectivity and biomedical systems through his work. He has received several prestigious awards, including the National Scholarship for Doctoral Students and the Outstanding Doctoral Dissertation Award from the Anhui Province Robotics Society.

Profile    

Orcid Profile

Google Scholar Profile 

Education 🎓

Dr. Shurun Wang has a robust academic background in Electrical Engineering and Automation. He completed his Ph.D. at the School of Electrical Engineering and Automation, Hefei University of Technology, in Hefei, China, from 2019 to 2024, where he specialized in biomedical signal analysis and brain function connectivity. During his doctoral studies, he also had the opportunity to enhance his research through a one-year visiting student program at the Graduate School of Medicine, Juntendo University, Tokyo, Japan, from April 2023 to April 2024. Prior to his Ph.D., Dr. Wang earned both his M.Sc. (2016–2019) and B.Sc. (2012–2016) degrees in Electrical Engineering and Automation, also from Hefei University of Technology, where he gained a strong foundation in electrical systems and automation technologies.

Professional Experience 💼

Dr. Shurun Wang is currently a Postdoctoral Researcher at the School of Information Science and Technology, University of Science and Technology of China (USTC), where he conducts cutting-edge research in biomedical signal analysis and brain function connectivity analysis. Prior to his postdoctoral role, he completed his Ph.D. at the School of Electrical Engineering and Automation, Hefei University of Technology. During his doctoral studies, Dr. Wang also undertook a one-year research stint as a visiting student at the Graduate School of Medicine, Juntendo University, Tokyo, Japan.

Research Interests 🔬

Dr. Wang’s primary research interests lie in biomedical signal analysis, particularly focusing on brain function connectivity. His work aims to develop advanced computational techniques to enhance the understanding of neural systems and brain activity patterns. This research is vital for applications in medical diagnostics, neuroengineering, and cognitive neuroscience, with potential contributions to improving treatments for neurological disorders and enhancing brain-machine interfaces.

Author Metrics 🏆

Dr. Wang has published extensively in top-tier journals and conferences, contributing to the fields of biomedical signal processing and neural networks. His work has gained recognition for its innovation and impact on both theoretical advancements and practical applications.

Awards and Recognition 🏆

  • National Scholarship for Doctoral Students
  • Outstanding Doctoral Dissertation Award from Anhui Province Robotics Society
  • Xplore New Automation Award 2018 of PHOENIX CONTACT

Academic Service

Dr. Wang has contributed significantly to the academic community by reviewing for over 10 reputable journals, including:

  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Transactions on Neural Networks and Learning Systems
  • Scientific Reports
  • Applied Artificial Intelligence

Publications Top Notes 📚

  1. Title: A novel approach to detecting muscle fatigue based on sEMG by using neural architecture search framework
    Authors: S Wang, H Tang, B Wang, J Mo
    Journal: IEEE Transactions on Neural Networks and Learning Systems
    Volume: 34, Issue: 8, Pages: 4932-4943
    Year: 2021
    Citations: 28
    Summary: This paper proposes a novel method for detecting muscle fatigue from surface electromyographic (sEMG) signals by employing a neural architecture search (NAS) framework. The study demonstrates that using NAS can efficiently identify optimal deep learning architectures for accurate and real-time fatigue detection, making it a significant contribution to health monitoring technologies.
  2. Title: Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy
    Authors: S Wang, H Tang, B Wang, J Mo
    Journal: Biomedical Signal Processing and Control
    Volume: 67, Article Number: 102510
    Year: 2021
    Citations: 24
    Summary: This study focuses on the analysis of muscle fatigue in the biceps brachii using rapid refined composite multiscale sample entropy (rRCMSE), a novel method to quantify the complexity of sEMG signals during fatigue. The research provides a reliable approach for muscle fatigue assessment in clinical and rehabilitation settings.
  3. Title: A double threshold adaptive method for robust detection of muscle activation intervals from surface electromyographic signals
    Authors: H Tang, S Wang, Q Tan, B Wang
    Journal: IEEE Transactions on Instrumentation and Measurement
    Volume: 71, Article Number: 1-12
    Year: 2022
    Citations: 5
    Summary: This paper introduces a double-threshold adaptive method to improve the robustness of detecting muscle activation intervals from sEMG signals. The method enhances the reliability and accuracy of muscle activation detection, which is crucial for fatigue monitoring and rehabilitation applications.
  4. Title: Continuous estimation of human joint angles from sEMG using a multi-feature temporal convolutional attention-based network
    Authors: S Wang, H Tang, L Gao, Q Tan
    Journal: IEEE Journal of Biomedical and Health Informatics
    Volume: 26, Issue: 11, Pages: 5461-5472
    Year: 2022
    Citations: 4
    Summary: This paper proposes a deep learning-based model that estimates human joint angles continuously from sEMG signals. The model uses a temporal convolutional attention mechanism to process multiple features, enabling precise real-time joint angle estimation for applications in rehabilitation and prosthetics.
  5. Title: Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms
    Authors: S Wang, H Tang, R Himeno, J Solé-Casals, CF Caiafa, S Han, S Aoki, …
    Journal: Computer Methods and Programs in Biomedicine
    Volume: 257, Article Number: 108419
    Year: 2022
    Citations: Not specified
    Summary: This paper focuses on optimizing graph neural network (GNN) architectures for predicting schizophrenia spectrum disorder. By using evolutionary algorithms, the study improves model accuracy, highlighting the potential of AI in mental health diagnosis and prognosis.

Conclusion

Dr. Shurun Wang is an outstanding candidate for the Best Researcher Award, owing to his remarkable contributions to biomedical signal analysis, brain function connectivity, and innovative health technologies. His research in detecting muscle fatigue, improving neurodiagnostic systems, and exploring neural systems for mental health prediction has the potential to revolutionize the field. With his continued dedication to advancing computational techniques in health sciences, Dr. Wang is poised to make even greater strides in improving medical diagnostics, neuroengineering, and treatment methodologies for neurological disorders.