Innovative Fusion in Neuron Classification: Shared Information from Human and Mouse Electrophysiological Data The scientific community has long faced significant challenges in neuron classification. Accurate classification of neurons is crucial for understanding brain function in both healthy and diseased states. This study, led by Ofek Ophir, Orit...

A New Breakthrough in Neuroinformatics: Research Report on Cadence Tool for Calcium Event Detection Background Introduction Calcium imaging technology has revolutionized the study of neuron ensembles, providing researchers with a powerful tool to simultaneously visualize and monitor multiple neuronal activities. Calcium imaging utilizes fluorescent...

Academic Background It is widely recognized that the cognitive function of patients with high-grade glioma is affected by the location and volume of the tumor. However, research on how to accurately predict individual patients’ cognitive function for personalized treatment decisions before and after surgery remains limited. Currently, most studies ...

MRQA: Addressing the Widespread Problem of MRI Protocol Non-Compliance Background In recent years, large-scale neuroimaging datasets have played a crucial role in studying brain-behavior relationships, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Human Connectome Project (HCP), and Adolescent Brain Cognitive Development (ABCD) st...

Image Classification Based on Bayesian Tensor Modeling for Alzheimer’s Disease Introduction Neuroimaging research is a crucial component of contemporary neuroscience, significantly enhancing our understanding of brain structure and function. Through these non-invasive visualization techniques, researchers can more accurately predict the risk of cer...

Identifying Diagnostic Biomarkers for Autism Spectrum Disorder using the PED Algorithm In the field of neuroinformatics, research on Autism Spectrum Disorder (ASD) predominantly focuses on the bidirectional connectivity between brain regions, with fewer studies addressing higher-order interaction anomalies among brain regions. To explore the comple...

Research Report on the Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation Academic Background Magnetic Resonance Imaging (MRI) plays a vital role in neurology, particularly in the precise segmentation of brain tissue. Accurate tissue segmentation is crucial for diagnosing brain injuries and neurodegenerative diseases. Segmenting...

Multi-Year, Multi-Disciplinary Efforts in Scientific Research Data Management Education Research Background With the development of modern neuroscience, Research Data Management (RDM) has become an indispensable skill for scientists. However, despite the importance of research data management for scientific research, such technical skills are often...

MRIO: A Magnetic Resonance Imaging Acquisition and Analysis Ontology Magnetic Resonance Imaging (MRI) is a biomedical imaging technology used to non-invasively visualize internal structures of tissues in three-dimensional space. MRI is widely used in studying the structure and function of the human brain and is a powerful tool for diagnosing neurol...

Neurohackademy: Combining Online and Offline Neurological Informatics Education Background Introduction In recent years, human neuroscience has entered an era of big data. Due to initiatives like the Human Connectome Project and the Adolescent Brain Cognitive Development (ABCD) study, scientists have acquired datasets of previously unimaginable sca...