Biomedical engineers have developed an automated process that can trace the shapes of active neurons as accurately as human researchers can, but in a fraction of the time.
This new technique, based on using artificial intelligence to interpret video images, addresses a critical roadblock in neuron analysis, allowing researchers to rapidly gather and process neuronal signals for real-time behavioral studies.
The research appeared in the Proceedings of the National Academy of Sciences.
To measure neural activity, researchers typically use a process known as two-photon calcium imaging, which allows them to record the activity of individual neurons in the brains of live animals. These recordings enable researchers to track which neurons are firing, and how they potentially correspond to different behaviors.
While these measurements are useful for behavioral studies, identifying individual neurons in the recordings is a painstaking process. Currently, the most accurate method requires a human analyst to circle every 'spark' they see in the recording, often requiring them to stop and rewind the video until the targeted neurons are identified and saved. To further complicate the process, investigators are often interested in identifying only a small subset of active neurons that overlap in different layers within the thousands of neurons that are imaged.
This process, called segmentation, is fussy and slow. A researcher can spend anywhere from four to 24 hours segmenting neurons in a 30-minute video recording, and that's assuming they're fully focused for the duration and don't take breaks to sleep, eat or use the bathroom.
In contrast, a new open source automated algorithm developed by image processing and neuroscience researchers can accurately identify and segment neurons in minutes.
"As a critical step towards complete mapping of brain activity, we were tasked with the formidable challenge of developing a fast automated algorithm that is as accurate as humans for segmenting a variety of active neurons imaged under different experimental settings," said the senior author.
"The data analysis bottleneck has existed in neuroscience for a long time -- data analysts have spent hours and hours processing minutes of data, but this algorithm can process a 30-minute video in 20 to 30 minutes," said another author. "We were also able to generalize its performance, so it can operate equally well if we need to segment neurons from another layer of the brain with different neuron size or densities."
"Our deep learning-based algorithm is fast, and is demonstrated to be as accurate as (if not better than) human experts in segmenting active and overlapping neurons from two-photon microscopy recordings," said the first author on the paper.
Deep-learning algorithms allow researchers to quickly process large amounts of data by sending it through multiple layers of nonlinear processing units, which can be trained to identify different parts of a complex image. In their framework, this team created an algorithm that could process both spatial and timing information in the input videos. They then 'trained' the algorithm to mimic the segmentation of a human analyst while improving the accuracy.
The advance is a critical step towards allowing neuroscientists to track neural activity in real time. Because of their tool's widespread usefulness, the team has made their software and annotated dataset available online.
https://pratt.duke.edu/about/news/artificial-intelligence-singles-out-neurons-faster-human-can
https://www.pnas.org/content/early/2019/04/10/1812995116
Artificial intelligence to map neurons faster than a human can
- 503 views
- Added
Edited
Latest News
Using health records and not genetic data to calculate genetic links between diseases
Vasomotion is critical in clearing amyloid from the brain
Artificial intelligence (AI) to help doctors identify cancer cells
A key protein linked to ageing identified
Transporting large drug molecules into cells via nanoparticles
Other Top Stories
Link between antidepressant use and congenital anomalies or stillbirths
RNA splicing factor and ageing
Rhythm of breathing affects memory and fear
A novel compound to alleviate pain and itch identified!
New pathways to treat non-alcoholic fatty-liver disease
Protocols
Dual-Angle Protocol for Doppler Optical Coherence Tomography to Improve Retinal Blood Flow Measur…
Detection of protein SUMOylation in vivo
In vivo analysis of protein sumoylation induced by a viral protein: Detection of HCMV pp71-induce…
Determination of SUMOylation sites
miR-Selection 3'UTR Target Selection Kit
Publications
Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-s…
Estimating heritability and genetic correlations from large health datasets in the absence of gen…
ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a…
CSB promoter downregulation via histone H3 hypoacetylation is an early determinant of replicative…
Carboxylated branched poly(-amino ester) nanoparticles enable robust cytosolic protein delivery…
Presentations
Hypoxia Inducible Factor - 1 (HIF-1)
Intracellular Protein Degradation
Pathophysiology of Type 1 Diabetes
Plant Viruses
Regulation by changes in chromatin structure
Posters
AACC-2018-Infectious Disease
AACC-2018-Mass Spectrometry Applications
AACC-2018-Lipids/Lipoproteins
AACC-2018-Management
AACC-2018-Immunology-abstracts