Illuminating the Activated Brain: Emerging Activity-Dependent Tools to Capture and Control Functional Neural Circuits.
10.1007/s12264-018-0291-x
- Author:
Qiye HE
1
;
Jihua WANG
2
;
Hailan HU
3
Author Information
1. Center for Neuroscience, and Department of Psychiatry of First Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, China. qhe@zju.edu.cn.
2. Center for Neuroscience, and Department of Psychiatry of First Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, China.
3. Center for Neuroscience, and Department of Psychiatry of First Affiliated Hospital, NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, China. huhailan@zju.edu.cn.
- Publication Type:Journal Article
- Keywords:
Activity-dependent tools;
Arc;
Emotion;
Immediate-early gene;
Neural ensembles;
c-fos
- MeSH:
Animals;
Brain;
metabolism;
Gene Expression Profiling;
methods;
Genes, Immediate-Early;
Humans;
Molecular Imaging;
methods;
Neural Pathways;
metabolism;
Neurons;
metabolism;
Signal-To-Noise Ratio
- From:
Neuroscience Bulletin
2019;35(3):369-377
- CountryChina
- Language:English
-
Abstract:
Immediate-early genes (IEGs) have long been used to visualize neural activations induced by sensory and behavioral stimuli. Recent advances in imaging techniques have made it possible to use endogenous IEG signals to visualize and discriminate neural ensembles activated by multiple stimuli, and to map whole-brain-scale neural activation at single-neuron resolution. In addition, a collection of IEG-dependent molecular tools has been developed that can be used to complement the labeling of endogenous IEG genes and, especially, to manipulate activated neural ensembles in order to reveal the circuits and mechanisms underlying different behaviors. Here, we review these techniques and tools in terms of their utility in studying functional neural circuits. In addition, we provide an experimental strategy to measure the signal-to-noise ratio of IEG-dependent molecular tools, for evaluating their suitability for investigating relevant circuits and behaviors.
