Title | Automating Event-detection of Brain Neuron Synaptic Activity and Action Potential Firing in vivo using a Random-access Multiphoton Laser Scanning Microscope for Real-time Analysis. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Sakaki KDR, Coleman P, Toth TDellazizzo, Guerrier C, Haas K |
Journal | Conf Proc IEEE Eng Med Biol Soc |
Volume | 2018 |
Pagination | 1-7 |
Date Published | 2018 07 |
ISSN | 1557-170X |
Keywords | Action Potentials, Animals, Brain, Models, Neurological, Neuronal Plasticity, Neurons, Software, Xenopus laevis |
Abstract | Determining how a neuron computes requires an understanding of the complex spatiotemporal relationship between its input (e.g. synaptic input as a result of external stimuli) and action potential output. Recent advances in in vivo, laser-scanning multiphoton technology, known as random-access microscopy (RAM), can capture this relationship by imaging fluorescent light, emitted from calcium-sensitive biosensors responding to synaptic and action potential firing in a neuron's full dendritic arbor and cell body. Ideally, a continuous output of fluorescent intensities from the neuron would be converted to a binary output (`event', 'or no-event'). These binary events can be used to correlate temporal and spatial associations between the input and output. However, neurons contain hundreds-to-thousands of synapses on the dendritic arbors generating an enormous quantity of data composed of physiological signals, which vary greatly in shape and size. Thus, automating data-processing tasks is essential to support high-throughput analysis for real-time/post-processing operations and to improve operators' comprehension of the data used to decipher neuron computations. Here, we describe an automated software algorithm to detect brain neuron events in real-time using an acousto-optic, multiphoton, laser scanning RAM developed in our laboratory. The fluorescent light intensities, from a genetically encoded, calcium biosensor (GCAMP 6m), are measured by our RAM system and are input to our 'event-detector', which converts them to a binary output meant for real-time applications. We evaluate three algorithms for this purpose: exponentially weighted moving average, cumulative sum, and template matching; present each algorithm's performance; and discuss user-feasibility of each. We validated our system in vivo, using the visual circuit of the Xenopus laevis. |
URL | https://doi.org/10.1109/EMBC.2018.8512983 |
DOI | 10.1109/EMBC.2018.8512983 |
Alternate Journal | Conf Proc IEEE Eng Med Biol Soc |
PubMed ID | 30440280 |
Grant List | / / CIHR / Canada |