What is it about?
The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with nearinfrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions.
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Why is it important?
The effective use of machine learning and near-infrared catecholamine nanosensors (nIRCat) to understand dopamine modulation.
Perspectives
Machine learning was able to differentiate between neuroanatomical regions or between neurotypical and disease states, with features not detectable by conventional statistical analysis.
Siamak Sorooshyari
Stanford University
Read the Original
This page is a summary of: Identifying Neural Signatures of Dopamine Signaling with Machine Learning, ACS Chemical Neuroscience, June 2023, American Chemical Society (ACS),
DOI: 10.1021/acschemneuro.3c00001.
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