Apoorva Safai | Neuroscience | Best Researcher Award

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Dr. Apoorva Safai | Neuroscience | Best Researcher Award 

Postdoctoral Research Associate, at University of Wisconsin-Madison, United States.

Dr. Apoorva Safai is a distinguished researcher specializing in neuroimaging, with a focus on deep learning applications in medical imaging and multimodal MRI analysis. She is currently a Postdoctoral Research Associate at the Integrating Diagnostics and Analytics (IDiA) Lab at the University of Wisconsin–Madison. Throughout her career, Dr. Safai has contributed significantly to understanding neurological disorders, particularly Parkinson’s disease and Alzheimer’s disease. Her research integrates advanced imaging techniques with machine learning to uncover intricate patterns in brain connectivity and structure. Dr. Safai’s work has been recognized through various awards and grants, underscoring her commitment to advancing medical imaging and neurodegenerative disease research. ​Idia Labs

Professional Profile

Scopus

ORCID

Google scholar

Education 🎓

Dr. Safai’s academic journey began with a Bachelor of Engineering in Electronics Engineering from P.V.P.I.T College, University of Pune, where she graduated with 65.4% marks in 2012. She pursued a Master of Technology in Biomedical Engineering at VIT University, Vellore, achieving a CGPA of 8.49 in 2015. Her passion for research led her to earn a PhD in Engineering from Symbiosis International University, Pune, between 2018 and 2023. Her doctoral research focused on developing a multimodal brain connectomic framework employing graph attention networks on structural and functional brain data, aiming to enhance the prediction and understanding of neurological disorders. ​Idia Labs

Experience 🧠

Dr. Safai’s professional experience is rich and diverse. Since April 2023, she has been serving as a Postdoctoral Research Associate at the IDiA Lab, University of Wisconsin–Madison, focusing on deep learning applications in optical coherence tomography and neuroimaging. Prior to this, she was a Senior Research Fellow and PhD Scholar at the Symbiosis Centre for Medical Image Analysis, Pune, from September 2021 to May 2022, where she worked on multimodal MRI analysis and deep learning models for neurological disorders. She also held the position of Technical Assistant in Imaging at the Department of Neuroimaging, NIMHANS, Bangalore, from May 2017 to August 2018, contributing to the Indo-UK project cVEDA, focusing on fMRI data acquisition and analysis. ​Idia Labs+1Google Scholar+1

Research Interests 🔬

Dr. Safai’s research interests lie at the intersection of neuroimaging and artificial intelligence. She specializes in multimodal MRI analysis, aiming to integrate various imaging modalities to provide a comprehensive understanding of brain structure and function. Her work in deep learning in medical imaging seeks to develop algorithms that can assist in the early detection and monitoring of neurological disorders such as Parkinson’s and Alzheimer’s diseases. By leveraging advanced computational techniques, Dr. Safai aims to uncover biomarkers and patterns that can lead to better diagnosis and treatment strategies for these conditions. ​

Awards 🏆

Dr. Safai’s contributions to neuroimaging and medical imaging have been recognized through several prestigious awards. She received the Alzheimer’s Association Research Fellowship to Promote Diversity (AARF-D) grant for 2025–2027, supporting her project titled “Multimodal AI-based Predictor of Alzheimer’s Disease (MAP-AD).” In 2020, she was awarded the ISMRM student research exchange grant for her proposal on high temporal resolution fMRI acquisition and advanced analysis for identifying reliable imaging markers for Parkinson’s disease. Additionally, she received a travel grant from the Movement Disorder Society for the MDS Conference in 2019 and an educational stipend from ISMRM for the same year’s conference, highlighting her active engagement and recognition in the scientific community. ​

Top Noted Publications 📚

  • Microstructural abnormalities of substantia nigra in Parkinson’s disease: A neuromelanin sensitive MRI atlas-based study

    • Year: 2020

    • Journal: Human Brain Mapping

    • Citations: 35 (PubMed)

    • Summary: This study investigates microstructural changes in the substantia nigra of Parkinson’s disease patients using neuromelanin-sensitive MRI, providing an atlas-based approach for assessing disease-related abnormalities.

  • Multimodal brain connectomics-based prediction of Parkinson’s disease using graph attention networks

    • Year: 2022

    • Journal: Frontiers in Neuroscience

    • Citations: 16 (Google Scholar)

    • Summary: The research utilizes graph attention networks (GATs) to analyze multimodal brain connectomics data for predicting Parkinson’s disease, demonstrating the effectiveness of deep learning in neurological disorder classification.

  • Disrupted structural connectome and neurocognitive functions in Duchenne muscular dystrophy: classifying and subtyping based on Dp140 dystrophin isoform

    • Year: 2022

    • Journal: Journal of Neurology

    • Citations: 11 (Loop)

    • Summary: This study explores the relationship between structural brain connectivity disruptions and neurocognitive deficits in Duchenne muscular dystrophy, with a focus on the Dp140 dystrophin isoform for patient subtyping.

  • Developing a radiomics signature for supratentorial extra-ventricular ependymoma using multimodal MR imaging

    • Year: 2021

    • Journal: Frontiers in Neurology

    • Citations: 5 (Google Scholar)

    • Summary: The research develops a radiomics-based approach using multimodal MRI to characterize supratentorial extra-ventricular ependymoma, enhancing tumor classification and diagnosis.

  • Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models

    • Year: 2024

    • Journal: Investigative Ophthalmology & Visual Science

    • Summary: This study compares the performance of 12 deep learning models in quantifying geographic atrophy in age-related macular degeneration, assessing their accuracy and reliability for clinical applications.

Conclusion

Apoorva Safai is a highly qualified candidate for the Best Researcher Award based on her strong academic background, impactful research, prestigious grants, and leadership in medical imaging and deep learning. Addressing minor improvements in authorship and funding scale would further elevate her profile. Overall, she is an excellent contender for the award.

Linjing Mu | Brain PET Imaging Award | Best Researcher Award

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Dr. Linjing Mu | Brain PET Imaging Award | Best Researcher Award

Dr. Linjing Mu, ETH Zurich, Switzerland

🔬 Dr. Linjing Mu pursued doctoral research at Nankai University, followed by a post-doctoral fellowship at Basel University, Switzerland. With expertise in radiopharmaceutical sciences, Dr. Mu led research teams at University Hospital Zurich and ETH Zurich, focusing on lead structure discovery, ligand synthesis, and radiolabeling method development. A prolific author, Dr. Mu has 106 peer-reviewed publications, including 10 as last and corresponding author, and 19 as the first author. Co-investigator on 10 patents, Dr. Mu has presented internationally and collaborated with industry leaders like Bayer Schering, Novartis, and Roche, contributing significantly to drug discovery and clinical translations.

 

Publication Profile:

Scopus

Orcid

Education:

Doctoral Research in Chemistry Department, Nankai University (1993-1996)

Post-Doctoral Fellow, Basel University, Switzerland (1996-2001)

Experience:

Team Leader in the field of Radiopharmaceutical Sciences, Group of Prof. Schubiger (2001-2010)

Head of Research, Radiopharmacy, University Hospital Zurich (2010-2021)

Senior Scientist, Prof. Schibli’s Group, ETH Zurich (2021-Present)

Research Expertise:

Lead Structure Finding based on Literature and Patents

Design and Synthesis of Novel Ligands

Structure-Activity Relationship Studies

Radiolabeling Method Development

In Vitro and In Vivo Evaluation

Academic Achievements:

Co-authored 106 Peer-Reviewed Publications

Last and Corresponding Author on 10, First Author on 19 (H-Factor 29, Scopus)

Co-Investigator on 10 Patent Applications

Contributed to International Conferences as Invited Speaker, Oral and Poster Presenter

Research Focus:

🔬 Dr. Linjing Mu’s research focus lies at the intersection of neuroimaging and drug development, particularly in the field of molecular imaging for neurodegenerative diseases like Alzheimer’s. Their work encompasses the development and optimization of radioligands targeting specific molecular pathways implicated in neurodegeneration. Through PET/MRI studies and the exploration of novel PET tracers, such as (R)-[18F]YH134 and (R)-[18F]PSS232, Dr. Mu aims to deepen our understanding of disease mechanisms and identify potential therapeutic targets. This research not only sheds light on disease pathology but also paves the way for the development of new diagnostic tools and pharmacological interventions.

 

Publication Top Notes:

📄 Wang, J. et al. Metabotropic glutamate receptor 5 (mGluR5) is associated with neurodegeneration and amyloid deposition in Alzheimer’s disease: A [18F]PSS232 PET/MRI study. Published in Alzheimer’s Research and Therapy, 2024. Cited by: 0.

📄 He, Y. et al. Identification of (R)-[18F]YH134 for Monoacylglycerol Lipase Neuroimaging and Exploration of Its Use for Central Nervous System and Peripheral Drug Development. Published in Journal of Nuclear Medicine, 2024. Cited by: 0.

📄 Boccalini, C. et al. The impact of tau deposition and hypometabolism on cognitive impairment and longitudinal cognitive decline. Published in Alzheimer’s and Dementia, 2024. Cited by: 1.

📄 Trachsel, B. et al. Reducing kidney uptake of radiolabelled exendin-4 using variants of the renally cleavable linker MVK. Published in EJNMMI Radiopharmacy and Chemistry, 2023. Cited by: 0.

📄 Lu, Y. et al. Proof-of-concept optimization of a copper-mediated 18F-radiosynthesis of a novel MAGL PET tracer on a high-throughput microdroplet platform and its macroscale translation. Published in Lab on a Chip, 2023. Cited by: 1.

📄 Ni, R. et al. Imaging increased metabolism in the spinal cord in mice after middle cerebral artery occlusion. Published in Photoacoustics, 2023. Cited by: 0.

📄 Zechner, M. et al. In Vitro and In Vivo Evaluation of ABCG2 (BCRP) Inhibitors Derived from Ko143. Published in Journal of Medicinal Chemistry, 2023. Cited by: 1.

📄 Bengs, S. et al. Rest/stress myocardial perfusion imaging by positron emission tomography with 18F-Flurpiridaz: A feasibility study in mice. Published in Journal of Nuclear Cardiology, 2023. Cited by: 3.

📄 Peretti, D.E. et al. ATN profile classification across two independent prospective cohorts. Published in Frontiers in Medicine, 2023. Cited by: 0.

📄 He, Y. et al. Multi-parameter optimization: Development of a morpholin-3-one derivative with an improved kinetic profile for imaging monoacylglycerol lipase in the brain. Published in European Journal of Medicinal Chemistry, 2022. Cited by: 2.