In the webinar titled, “Improved detection of c-fos labeled and pyramidal neurons using deep machine learning in NeuroInfo,” Dr. Gerfen, joined by Dr. Brian Eastwood and Dr. Nathan O’Connor, demonstrates how NeuroInfo uses deep learning neuronal networks to successfully detect pyramidal neurons in multiple brain regions.

Dr. Gerfen nicely summarizes the starting point of the cell detection algorithm: “The way to think about it, is that it’s a topographic map of a mountain range. Things that are very bright have very high intensity levels (e.g., peaks) relative to their surrounding areas. That’s what this software picks out.”

With that criterion as a starting point, all detected objects–which can include somas, axons, dendrites, and fiber bundles–are passed into the neural network which has been trained to identify pyramidal cell bodies. According to Dr. O’Connor, over 90 thousand labeled images were used to teach NeuroInfo’s neural network how to recognize pyramidal neurons. The end result is a neural network that contains millions of parameters able to distinguish pyramidal neurons from all the other features in an image.

“We automatically adjust millions of parameters during network training,” says computational neuroscientist Dr. Eastwood. “The training process updates these parameters to improve how the neural network identifies pyramidal cells in images. During training, we know the answers. So, we use that knowledge to inform the network about true and false outcomes to update network parameters.”. Neural networks become “smarter” and more refined as they encounter more training data. The MBF Bioscience development team leverages this to create increasingly robust neural networks for detecting a variety of cell and tissue types as we work with data from laboratories.

“The networks are getting better and smarter as we provide more types of neurons from different images of brain sections into the training sets, so that the networks are able to identify not only different types of neurons but distinguish labeled neurons from histologic processing artifacts.” says Dr. Gerfen.

You can try this advanced AI method for cell detection in our NeuroInfo software. If you have any questions about deep learning in neuroscience, email us at info@mbfbioscience.com, or join the discussion at forums.mbfbioscience.com.

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Analyzing cellular populations within specific anatomies in brain images requires expertise in both neuroanatomy and cellular identification. This typically involves a scientist comparing experimental images with a reference atlas and manually delineating anatomical regions and marking cell populations within. NeuroInfo^®, a revolutionary new technology from MBF Bioscience, enables researchers to automatically identify and delineate mouse brain regions based on the publicly available Allen Mouse Brain Reference Atlas.

“NeuroInfo has the potential to greatly improve our understanding of how mental disorders influence neuronal cell populations,” says Nathan O’Connor Ph.D., product manager at MBF Bioscience. “Because it makes identifying brain regions substantially faster and more accurate, researchers will be able to explore many more brain regions.”

“The Allen Mouse Brain Reference Atlas is a valuable tool to assist scientists in their research. We’re thrilled that MBF has chosen to integrate this resource into NeuroInfo,” stated Amy Bernard, Ph.D., Product Architect at the Allen Institute for Brain Science.

“Using this remarkable technology, neuroscientists will obtain more repeatable, objective analyses that have been possible to date. Thanks to the integration with the Allen Mouse Brain Reference Atlas, these analyses will be more standardized so that they can be compared across experiments and laboratories,” says Jack Glaser, President.

NeuroInfo can be used with MBF Bioscience’s slide scanning software and virtually all commercial whole slide scanners. The data from NeuroInfo seamlessly integrates with MBF Bioscience’s products including Neurolucida, Stereo Investigator, Biolucida, and BrainMaker.

The tools in NeuroInfo allow researchers to automatically delineate anatomies in the experimental specimens, and detect cells within these anatomies. NeuroInfo yields data that can be invaluable to better understand the organization and composition of the nervous system, and to further knowledge in neurogenomics, transcriptomics, proteomics, and connectomics.

The National Institute of Mental Health provides funding to support the development of NeuroInfo.

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