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In JoVE (1)
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Articles by Jaerock Kwon in JoVE
JoVE Bioengineering
चाकू बढ़त स्कैनिंग माइक्रोस्कोपी के लिए नमूना तैयार करना, इमेजिंग और विश्लेषण प्रोटोकॉल
1Department of Computer Science and Engineering, Texas A&M University, 2Beckman Institute for Advanced Science and Technology, University of Illinois, 3Department of Electrical and Computer Engineering, Kettering University, 43Scan, 5Department of Veterinary Integrative Biosciences, Texas A&M University
मस्तिष्क नमूना तैयारी से धारावाहिक सेक्शनिंग स्कैन चाकू - एज माइक्रोस्कोप का उपयोग कर डेटा दृश्य और विश्लेषण करने के लिए, इमेजिंग के लिए पूरी प्रक्रिया में वर्णित है. वर्तमान में इस तकनीक के लिए माउस मस्तिष्क डेटा प्राप्त करने के लिए इस्तेमाल किया है, लेकिन यह अन्य अंगों, अन्य प्रजातियों के लिए लागू होता है.
Other articles by Jaerock Kwon on PubMed
Facilitating Neural Dynamics for Delay Compensation: a Road to Predictive Neural Dynamics?
Goal-directed behavior is a hallmark of cognition. An important prerequisite to goal-directed behavior is that of prediction. In order to establish a goal and devise a plan, one needs to see into the future and predict possible future events. Our earlier work has suggested that compensation mechanisms for neuronal transmission delay may have led to a preliminary form of prediction. In that work, facilitating neuronal dynamics was found to be effective in overcoming delay (the Facilitating Activation Network model, or FAN). The extrapolative property of the delay compensation mechanism can be considered as prediction for incoming signals (predicting the present based on the past). The previous FAN model turns out to have a limitation especially when longer delay needs to be compensated, which requires higher facilitation rates than FAN's normal range. We derived an improved facilitating dynamics at the neuronal level to overcome this limitation. In this paper, we tested our proposed approach in controllers for 2D pole balancing, where the new approach was shown to perform better than the previous FAN model. Next, we investigated the differential utilization of facilitating dynamics in sensory vs. motor neurons and found that motor neurons utilize the facilitating dynamics more than the sensory neurons. These findings are expected to help us better understand the role of facilitating dynamics in delay compensation, and its potential development into prediction, a necessary condition for goal-directed behavior.
Fast Macro-scale Transmission Imaging of Microvascular Networks Using KESM
Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research. We are able to achieve ≈ 0.7μm resolution laterally with 1μm depth resolution using serial sectioning. No alignment was necessary and contrast was sufficient to allow segmentation and measurement of vessels.
Multiscale Exploration of Mouse Brain Microstructures Using the Knife-edge Scanning Microscope Brain Atlas
Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.
