Статті в журналах: "Behavior Imaging"

1

McKeown, Joseph T., Amy J. Clarke, and Jörg M. K. Wiezorek. "Imaging transient solidification behavior." MRS Bulletin 45, no. 11 (November 2020): 916–26. http://dx.doi.org/10.1557/mrs.2020.273.

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2

Moreau, Paul-Antoine, Ermes Toninelli, Thomas Gregory, Reuben S. Aspden, Peter A. Morris, and Miles J. Padgett. "Imaging Bell-type nonlocal behavior." Science Advances 5, no. 7 (July 2019): eaaw2563. http://dx.doi.org/10.1126/sciadv.aaw2563.

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The violation of a Bell inequality not only attests to the nonclassical nature of a system but also holds a very unique status within the quantum world. The amount by which the inequality is violated often provides a good benchmark on how a quantum protocol will perform. Acquiring images of such a fundamental quantum effect is a demonstration that images can capture and exploit the essence of the quantum world. Here, we report an experiment demonstrating the violation of a Bell inequality within observed images. It is based on acquiring full-field coincidence images of a phase object probed by photons from an entangled pair source. The image exhibits a violation of a Bell inequality with S = 2.44 ± 0.04. This result both opens the way to new quantum imaging schemes based on the violation of a Bell inequality and suggests promise for quantum information schemes based on spatial variables.

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3

Uzel, Kerem, and Manuel Zimmer. "Imaging the Emergence of Behavior." Cell 179, no. 2 (October 2019): 285–86. http://dx.doi.org/10.1016/j.cell.2019.09.006.

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4

Kedrin, Dmitriy, Bojana Gligorijevic, Jeffrey Wyckoff, Vladislav V. Verkhusha, John Condeelis, Jeffrey E. Segall, and Jacco van Rheenen. "Intravital imaging of metastatic behavior through a mammary imaging window." Nature Methods 5, no. 12 (November 9, 2008): 1019–21. http://dx.doi.org/10.1038/nmeth.1269.

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5

Fung, Lawrence K. "Can imaging predict future suicidal behavior?" Science Translational Medicine 8, no. 355 (September 7, 2016): 355ec143. http://dx.doi.org/10.1126/scitranslmed.aah6433.

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6

Chapman, Donna J. "Breastfeeding, Brain Imaging, and Maternal Behavior." Journal of Human Lactation 27, no. 3 (July 25, 2011): 304–5. http://dx.doi.org/10.1177/0890334411410586.

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7

Higashijima, Shin-ichi, Mark A. Masino, Gail Mandel, and Joseph R. Fetcho. "Imaging Neuronal Activity During Zebrafish Behavior With a Genetically Encoded Calcium Indicator." Journal of Neurophysiology 90, no. 6 (December 2003): 3986–97. http://dx.doi.org/10.1152/jn.00576.2003.

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Genetically encoded calcium indicators, such as cameleon, have offered the promise of noninvasively monitoring activity of neurons, but no one has demonstrated whether these indicators can report calcium transients in neurons of behaving vertebrates. We show that cameleon can be expressed at high levels in sensory and spinal cord neurons in zebrafish by using neural-specific promoters in both transient expression experiments and in a stable transgenic line. Using standard confocal microscopy, calcium transients in identified motoneurons and spinal interneurons could be detected during escape behaviors produced by a touch on the head of the fish. Small movements of the restrained fish during the behavior did not represent a major problem for analyzing the calcium responses because of the ratiometric nature of cameleon. We conclude that cameleon can be used to noninvasively study the activity of neurons in an intact, behaving vertebrate. The ability to introduce an indicator genetically allows for studies of the functional roles of local interneurons that cannot easily be monitored with other approaches. Transgenic lines such as the one we generated can also be crossed into mutant lines of fish to study both structural and functional consequences of the mutations.

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8

Levin, Aaron. "Imaging Studies May Someday Help Predict Behavior." Psychiatric News 45, no. 12 (June 18, 2010): 14. http://dx.doi.org/10.1176/pn.45.12.psychnews_45_12_026.

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9

MACREADY, NORRA. "Imaging Can Match Brain Structure to Behavior." Pediatric News 39, no. 3 (March 2005): 27. http://dx.doi.org/10.1016/s0031-398x(05)70041-2.

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10

Day, Richard N. "Imaging protein behavior inside the living cell." Molecular and Cellular Endocrinology 230, no. 1-2 (January 2005): 1–6. http://dx.doi.org/10.1016/j.mce.2004.10.011.

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11

Iacoboni, M., E. Zaidel, N. Sicotte, R. P. Woods, M. S. Cohen, and J. C. Mazziotta. "Waves of Endogenous Context: Behavior and Imaging." NeuroImage 7, no. 4 (May 1998): S953. http://dx.doi.org/10.1016/s1053-8119(18)31786-5.

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12

Arnaldi, D. "Brain imaging in rem sleep behavior disorder." Journal of the Neurological Sciences 405 (October 2019): 44. http://dx.doi.org/10.1016/j.jns.2019.10.122.

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13

Saykin, Andrew J. "Brain Imaging and Behavior: Progress and Opportunities." Brain Imaging and Behavior 1, no. 1-2 (May 26, 2007): 1–2. http://dx.doi.org/10.1007/s11682-007-9005-0.

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14

MACREADY, NORRA. "Imaging Can Match Brain Structure to Behavior." Clinical Psychiatry News 33, no. 2 (February 2005): 36. http://dx.doi.org/10.1016/s0270-6644(05)70690-1.

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15

Li, Wenjun, Jason M. Gauthier, and Daniel Kreisel. "Intravital imaging of leukocyte behavior in atherosclerosis." Aging 11, no. 3 (January 23, 2019): 841–42. http://dx.doi.org/10.18632/aging.101792.

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16

Oesch, Lukas T., Mary Gazea, Thomas C. Gent, Mojtaba Bandarabadi, Carolina Gutierrez Herrera, and Antoine R. Adamantidis. "REM sleep stabilizes hypothalamic representation of feeding behavior." Proceedings of the National Academy of Sciences 117, no. 32 (July 30, 2020): 19590–98. http://dx.doi.org/10.1073/pnas.1921909117.

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During rapid eye movement (REM) sleep, behavioral unresponsiveness contrasts strongly with intense brain-wide neural network dynamics. Yet, the physiological functions of this cellular activation remain unclear. Using in vivo calcium imaging in freely behaving mice, we found that inhibitory neurons in the lateral hypothalamus (LHvgat) show unique activity patterns during feeding that are reactivated during REM, but not non-REM, sleep. REM sleep-specific optogenetic silencing of LHvgatcells induced a reorganization of these activity patterns during subsequent feeding behaviors accompanied by decreased food intake. Our findings provide evidence for a role for REM sleep in the maintenance of cellular representations of feeding behavior.

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17

Walsh, Alex J., Katie Mueller, Isabel Jones, Tiffany M. Heaster, Krishanu Saha, and Melissa C. Skala. "Autofluorescence Imaging of T cell Activation." Journal of Immunology 200, no. 1_Supplement (May 1, 2018): 120.13. http://dx.doi.org/10.4049/jimmunol.200.supp.120.13.

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Abstract T cells can have different activities based on receptor expression and cytokine production. Current methods to classify and assess immune cell behavior include flow cytometry and immunohistochemistry, which require immune cell labeling and tissue fixation. A non-invasive method for determining T cell behavior is needed to study immune cell behaviors in tumors and evaluate novel immunotherapies. Activated T cells require high rates of glycolysis to maintain immune activities. Therefore, we are developing optical metabolic imaging (OMI) to assess the metabolic profile of T cell subtypes and activation states using cells isolated from human blood. OMI probes the fluorescence intensity and lifetime of the metabolic coenzymes NAD(P)H and FAD, to quantitate the redox state of the cell through the optical redox ratio (NAD(P)H fluorescence intensity divided by the sum of NAD(P)H and FAD fluorescence intensity) and co-enzyme binding. Our results show that the optical redox ratio is increased in activated populations of unsorted T cells, and of CD8+ T cells, consistently across four different donors. Single-cell analysis of the unsorted, unactivated T cell populations revealed a small portion of cells in an activated state. Inter-donor heterogeneity highlights the variability of immune responses between patients. These results indicate that OMI is a powerful tool for assessing T cell subtype and behavior. OMI utilizes the autofluorescent properties of NAD(P)H and FAD, and thus is contrast agent free, non-damaging, and requires no genetic manipulation. Therefore, OMI can be used to image T cell interactions with tumors in time-course studies of tumor development or assess the efficacy of immunotherapy.

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18

Luwei Tian, Luwei Tian, Ming Guo Ming Guo, and Xingtao Xu and Linfang Shi Xingtao Xu and Linfang Shi. "Imaging Serum Proteome Behavior in Process of Lead Transportation in Vivo: A Fluorescence Spectroscopic Analysis Insight." Journal of the chemical society of pakistan 44, no. 5 (2022): 453. http://dx.doi.org/10.52568/001122/jcsp/44.05.2022.

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As a common heavy metal, Pb (II) can react with biological macromolecules in the human body and have an impact on human health, but there are few studies on its synergistic interaction with a variety of proteins of different abundance. Pb (II) binding with a synthetic model protein system (bovine serum albumin (BSA) and bovine lactoferrin (BLF)) was characterized using fluorescence spectroscopy and was described using a quantitative model. Pb (II) quenched the fluorescence of BSA-BLF, indicating that Pb (II) interacted with the BSA-BLF protein system, and was affected by single protein, mixed proteins and the solution microenvironment. A model was constructed and an indicator of the interaction (FPI) was derived to quantify the interactions. There was a high correlation (R2=0.9182, pandlt;0.001) between the FPI and the Pb (II) concentration when the interaction models were analyzed with a Taylor function. The effects of the solvent microenvironment on BSA, BLF and BSA-BLF were evaluated using the IOM (overall microenvironmental influence factor). BLF was more easily affected by the solution microenvironment with increasing concentrations than BSA, while BSA-BLF was less affected.

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19

Wang, Baodong, Xiaofeng Jiang, Zihao Dong, and Jinping Li. "Behavioral Parameter Field for Human Abnormal Behavior Recognition in Low-Resolution Thermal Imaging Video." Applied Sciences 12, no. 1 (December 31, 2021): 402. http://dx.doi.org/10.3390/app12010402.

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In recent years, thermal imaging cameras are widely used in the field of intelligent surveillance because of their special imaging characteristics and better privacy protection properties. However, due to the low resolution and fixed location for current thermal imaging cameras, it is difficult to effectively identify human behavior using a single detection method based on skeletal keypoints. Therefore, a self-update learning method is proposed for fixed thermal imaging camera scenes, called the behavioral parameter field (BPF). This method can express the regularity of human behavior patterns concisely and directly. Firstly, the detection accuracy of small targets under low-resolution video is improved by optimizing the YOLOv4 network to obtain a human detection model under thermal imaging video. Secondly, the BPF model is designed to learn the human normal behavior features at each position. Finally, based on the learned BPF model, we propose to use metric modules, such as cosine similarity and intersection over union matching, to accomplish the classification of human abnormal behaviors. In the experimental stage, the living scene of the indoor elderly living alone is applied as our experimental case, and a variety of detection models are compared to the proposed method for verifying the effectiveness and practicability of the proposed behavioral parameter field in the self-collected thermal imaging dataset for the indoor elderly living alone.

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20

Porro, Carlo A. "Functional Imaging and Pain: Behavior, Perception, and Modulation." Neuroscientist 9, no. 5 (October 2003): 354–69. http://dx.doi.org/10.1177/1073858403253660.

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21

Nazarian, Ara, and Ralph Müller. "Time-lapsed microstructural imaging of bone failure behavior." Journal of Biomechanics 37, no. 1 (January 2004): 55–65. http://dx.doi.org/10.1016/s0021-9290(03)00254-9.

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22

Heikal, A. A., K. Wachowicz, and B. G. Fallone. "MTF behavior of compressed sensing MR spectroscopic imaging." Medical Physics 40, no. 5 (April 22, 2013): 052302. http://dx.doi.org/10.1118/1.4800642.

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23

Sayinti, A., A. Kaya, and A. Vertiy. "3D Imaging of Water Behavior at Millimeter Wavelength." Acta Physica Polonica A 123, no. 2 (February 2013): 467–69. http://dx.doi.org/10.12693/aphyspola.123.467.

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24

Sengel, Jason T., and Mark I. Wallace. "Imaging the dynamics of individual electropores." Proceedings of the National Academy of Sciences 113, no. 19 (April 25, 2016): 5281–86. http://dx.doi.org/10.1073/pnas.1517437113.

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Electroporation is a widely used technique to permeabilize cell membranes. Despite its prevalence, our understanding of the mechanism of voltage-mediated pore formation is incomplete; methods capable of visualizing the time-dependent behavior of individual electropores would help improve our understanding of this process. Here, using optical single-channel recording, we track multiple isolated electropores in real time in planar droplet interface bilayers. We observe individual, mobile defects that fluctuate in size, exhibiting a range of dynamic behaviors. We observe fast (25 s−1) and slow (2 s−1) components in the gating of small electropores, with no apparent dependence on the applied potential. Furthermore, we find that electropores form preferentially in the liquid disordered phase. Our observations are in general supportive of the hydrophilic toroidal pore model of electroporation, but also reveal additional complexity in the interactions, dynamics, and energetics of electropores.

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25

Edelman, Bradley Jay, and Emilie Macé. "Functional ultrasound brain imaging: Bridging networks, neurons, and behavior." Current Opinion in Biomedical Engineering 18 (June 2021): 100286. http://dx.doi.org/10.1016/j.cobme.2021.100286.

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26

Marcinkiewcz, Catherine, Kanza Khan, and Gabrielle Bierlein-De La Rosa. "Deconstructing Neural Networks Governing Social Behavior With 3D Imaging." Biological Psychiatry 89, no. 9 (May 2021): S387. http://dx.doi.org/10.1016/j.biopsych.2021.02.962.

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27

Krewer, C., J. Bergmann, F. Müller, and K. Jahn. "Brain imaging studies in pusher behavior: a narrative review." Neurologie & Rehabilitation 25, S1 (2019): 42–45. http://dx.doi.org/10.14624/nr1904008.

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28

KAMIYAMA, Naohisa. "Ultrasound Diagnostic Imaging by Using Nonlinear Behavior of Microbubbles." Journal of the Society of Mechanical Engineers 111, no. 1074 (2008): 408–11. http://dx.doi.org/10.1299/jsmemag.111.1074_408.

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29

Arslan, Ayla. "Genes, Brains, and Behavior: Imaging Genetics for Neuropsychiatric Disorders." Journal of Neuropsychiatry and Clinical Neurosciences 27, no. 2 (April 2015): 81–92. http://dx.doi.org/10.1176/appi.neuropsych.13080185.

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30

Immel, Erwin, and Andreas Melzer. "Improvement of the MR imaging behavior of vascular implants." Minimally Invasive Therapy & Allied Technologies 15, no. 2 (January 2006): 85–92. http://dx.doi.org/10.1080/13645700600641004.

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31

Seelig, Johannes D., M. Eugenia Chiappe, Gus K. Lott, Michael B. Reiser, and Vivek Jayaraman. "Calcium Imaging in Drosophila During Walking and Flight Behavior." Biophysical Journal 100, no. 3 (February 2011): 97a. http://dx.doi.org/10.1016/j.bpj.2010.12.735.

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32

Saykin, Andrew J. "Brain Imaging and Behavior: A Message from the Editor." Brain Imaging and Behavior 4, no. 1 (March 2010): 1–4. http://dx.doi.org/10.1007/s11682-010-9093-0.

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33

Manoharan, Dr Samuel. "Embedded Imaging System Based Behavior Analysis of Dairy Cow." June 2020 2, no. 2 (June 6, 2020): 148–54. http://dx.doi.org/10.36548/jei.2020.2.006.

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It is essential to develop ambient environmental conditions for counteracting the heat stress in dairy cows by efficient and reliable monitoring of the activities of the cow and existing environmental conditions. For this purpose, we present a system with an array of integrated sensor modules that continuously measure and record humidity and ambient temperature while simultaneously monitoring the dairy cows drinking behavior using a cost-efficient embedded imaging system. Video streams are collected by installing embedded imaging modules over the drinking troughs for testing and experimentation in the dairy farm. Convolutional neural network (CNN) model using deep learning techniques is used for analysis of the video stream by detection of the head of the dairy cow above the drinking trough. The values obtained as true positive rate and F1 score of the detection of the head of the cow are both 0.98. The dairy cows drinking behavior and the effect of heat stress is analyzed and recorded for varied environmental conditions over a period of twelve months. Based on the results of analysis, it is evident that the temperature and humidity index (THI) greatly influence the total frequency and length of everyday drinking habits of dairy cows. The drinking behavior of dairy cows and the effects of heat stress is demonstrated clearly using the automated imaging system with long-term monitoring and data collection. Quantitative assessment and automation are possible using this novel monitoring system to be implemented in dairy farms.

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34

Chirieleison, Steven M., Taylor A. Bissell, Christopher C. Scelfo, Jordan E. Anderson, Yong Li, Doug J. Koebler, and Bridget M. Deasy. "Automated live cell imaging systems reveal dynamic cell behavior." Biotechnology Progress 27, no. 4 (June 20, 2011): 913–24. http://dx.doi.org/10.1002/btpr.629.

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35

Padmanabhan, Aarthi, and Beatriz Luna. "Developmental imaging genetics: Linking dopamine function to adolescent behavior." Brain and Cognition 89 (August 2014): 27–38. http://dx.doi.org/10.1016/j.bandc.2013.09.011.

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36

Esposito, Gianluca, Paola Rigo, and Marc H. Bornstein. "Brain imaging technologies to study infant behavior and development." Infant Behavior and Development 60 (August 2020): 101461. http://dx.doi.org/10.1016/j.infbeh.2020.101461.

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37

Chaudhary, Umair J., Maria Centeno, David W. Carmichael, Christian Vollmar, Roman Rodionov, Silvia Bonelli, Jason Stretton, et al. "Imaging the interaction: Epileptic discharges, working memory, and behavior." Human Brain Mapping 34, no. 11 (June 19, 2012): 2910–17. http://dx.doi.org/10.1002/hbm.22115.

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38

Aguilar-Moreno, Alejandro, Juan Ortiz, Luis Concha, Sarael Alcauter, and Raúl G. Paredes. "Brain circuits activated by female sexual behavior evaluated by manganese enhanced magnetic resonance imaging." PLOS ONE 17, no. 8 (August 1, 2022): e0272271. http://dx.doi.org/10.1371/journal.pone.0272271.

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Magnetic resonance imaging (MRI) allows obtaining anatomical and functional information of the brain in the same subject at different times. Manganese-enhanced MRI (MEMRI) uses manganese ions to identify brain activity, although in high doses it might produce neurotoxic effects. Our aims were to identify a manganese dose that does not affect motivated behaviors such as sexual behavior, running wheel and the rotarod test. The second goal was to determine the optimal dose of chloride manganese (MnCl2) that will allow us to evaluate activation of brain regions after females mated controlling (pacing) the sexual interaction. To achieve that, two experiments were performed. In experiment 1 we evaluated the effects of two doses of MnCl2, 8 and 16 mg/kg. Subjects were injected with one of the doses of MnCl2 24 hours before the test on sessions 1, 5 and 10 and immediately thereafter scanned. Female sexual behavior, running wheel and the rotarod were evaluated once a week for 10 weeks. In experiment 2 we followed a similar procedure, but females paced the sexual interaction once a week for 10 weeks and were injected with one of the doses of MnCl2 24 hours before the test and immediately thereafter scanned on sessions 1, 5 and 10. The results of experiment 1 show that neither dose of MnCl2 induces alterations on sexual behavior, running wheel and rotarod. Experiment 2 demonstrated that MEMRI allow us to detect activation of different brain regions after sexual behavior, including the olfactory bulb (OB), the bed nucleus of the stria terminalis (BNST), the amygdala (AMG), the medial preoptic area (MPOA), the ventromedial hypothalamus (VMH), the nucleus accumbens (NAcc), the striatum (STR) and the hippocampus (Hipp) allowing the identification of changes in brain circuits activated by sexual behavior. The socio sexual circuit showed a higher signal intensity on session 5 than the reward circuit and the control groups indicating that even with sexual experience the activation of the reward circuit requires the activation of the socio sexual circuit. Our study demonstrates that MEMRI can be used repeatedly in the same subject to evaluate the activation of brain circuits after motivated behaviors and how can this activation change with experience.

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39

Nguyen, Jeffrey P., Frederick B. Shipley, Ashley N. Linder, George S. Plummer, Mochi Liu, Sagar U. Setru, Joshua W. Shaevitz, and Andrew M. Leifer. "Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans." Proceedings of the National Academy of Sciences 113, no. 8 (December 28, 2015): E1074—E1081. http://dx.doi.org/10.1073/pnas.1507110112.

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The ability to acquire large-scale recordings of neuronal activity in awake and unrestrained animals is needed to provide new insights into how populations of neurons generate animal behavior. We present an instrument capable of recording intracellular calcium transients from the majority of neurons in the head of a freely behaving Caenorhabditis elegans with cellular resolution while simultaneously recording the animal’s position, posture, and locomotion. This instrument provides whole-brain imaging with cellular resolution in an unrestrained and behaving animal. We use spinning-disk confocal microscopy to capture 3D volumetric fluorescent images of neurons expressing the calcium indicator GCaMP6s at 6 head-volumes/s. A suite of three cameras monitor neuronal fluorescence and the animal’s position and orientation. Custom software tracks the 3D position of the animal’s head in real time and two feedback loops adjust a motorized stage and objective to keep the animal’s head within the field of view as the animal roams freely. We observe calcium transients from up to 77 neurons for over 4 min and correlate this activity with the animal’s behavior. We characterize noise in the system due to animal motion and show that, across worms, multiple neurons show significant correlations with modes of behavior corresponding to forward, backward, and turning locomotion.

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40

Miri, Andrew, Kayvon Daie, Rebecca D. Burdine, Emre Aksay, and David W. Tank. "Regression-Based Identification of Behavior-Encoding Neurons During Large-Scale Optical Imaging of Neural Activity at Cellular Resolution." Journal of Neurophysiology 105, no. 2 (February 2011): 964–80. http://dx.doi.org/10.1152/jn.00702.2010.

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The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals.

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41

Hillman, Bruce J. "Churlish Behavior." Journal of the American College of Radiology 6, no. 9 (September 2009): 605. http://dx.doi.org/10.1016/j.jacr.2009.05.013.

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42

Li, Hua, Yuehua Lei, Tao Guo, Xiang Zhang, and Hong Zhu. "Functional Imaging in Improving Children’s Mental Health Based on Behavior." Computational and Mathematical Methods in Medicine 2022 (July 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/4774771.

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At present, children’s psychological and behavioral health care is mainly based on the doctor’s observation and diagnosis. On the whole, it is inefficient, and the effect of health care cannot meet the current needs of children’s behavior. Therefore, this paper uses the method of functional imaging to study the key factors of children’s behavioral health care. In this paper, the structure and function of children’s brain are associated with children’s psychological behavior. The brain structure of 64 children in our city is detected by functional image processing, and 64 children are divided into groups according to the detection results. According to the children’s performance, the children were divided into physical disorder (11 cases), emotional disorder (14 cases), cognitive disorder (12 cases), and normal group (42 cases). Among them, 3 cases had three kinds of disorders, 6 cases had both emotional and cognitive disorders, 7 cases had physical and emotional disorders, and 5 cases had physical and cognitive disorders. In this paper, according to the research data of functional imaging on different levels of children’s brain, we use computer to model and simulate through digital conversion technology, draw the neural network Atlas of children’s psychological behavior, compare the children’s representation and image characteristics according to functional imaging, and then, study the relationship between children’s signs and images, to make a plan for improving children’s psychological behavior health care. The study shows that in the above different groups, the linear correlation between the functional imaging results and the representation of 22 abnormal children is 98%, and the fuzzy deviation is only 3.52%, which indicates that functional imaging can be used as the basic judgment basis in improving children’s psychological and behavioral health care and can predict and reasonably prevent children’s potential psychological behavior according to the images.

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43

Wekselblatt, Joseph B., Erik D. Flister, Denise M. Piscopo, and Cristopher M. Niell. "Large-scale imaging of cortical dynamics during sensory perception and behavior." Journal of Neurophysiology 115, no. 6 (June 1, 2016): 2852–66. http://dx.doi.org/10.1152/jn.01056.2015.

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Sensory-driven behaviors engage a cascade of cortical regions to process sensory input and generate motor output. To investigate the temporal dynamics of neural activity at this global scale, we have improved and integrated tools to perform functional imaging across large areas of cortex using a transgenic mouse expressing the genetically encoded calcium sensor GCaMP6s, together with a head-fixed visual discrimination behavior. This technique allows imaging of activity across the dorsal surface of cortex, with spatial resolution adequate to detect differential activity in local regions at least as small as 100 μm. Imaging during an orientation discrimination task reveals a progression of activity in different cortical regions associated with different phases of the task. After cortex-wide patterns of activity are determined, we demonstrate the ability to select a region that displayed conspicuous responses for two-photon microscopy and find that activity in populations of individual neurons in that region correlates with locomotion in trained mice. We expect that this paradigm will be a useful probe of information flow and network processing in brain-wide circuits involved in many sensory and cognitive processes.

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Hiroshima, Michio, Masato Yasui, and Masahiro Ueda. "Large-scale single-molecule imaging aided by artificial intelligence." Microscopy 69, no. 2 (February 22, 2020): 69–78. http://dx.doi.org/10.1093/jmicro/dfz116.

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Abstract Single-molecule imaging analysis has been applied to study the dynamics and kinetics of molecular behaviors and interactions in living cells. In spite of its high potential as a technique to investigate the molecular mechanisms of cellular phenomena, single-molecule imaging analysis has not been extended to a large scale of molecules in cells due to the low measurement throughput as well as required expertise. To overcome these problems, we have automated the imaging processes by using computer operations, robotics and artificial intelligence (AI). AI is an ideal substitute for expertise to obtain high-quality images for quantitative analysis. Our automated in-cell single-molecule imaging system, AiSIS, could analyze 1600 cells in 1 day, which corresponds to ∼ 100-fold higher efficiency than manual analysis. The large-scale analysis revealed cell-to-cell heterogeneity in the molecular behavior, which had not been recognized in previous studies. An analysis of the receptor behavior and downstream signaling was accomplished within a significantly reduced time frame and revealed the detailed activation scheme of signal transduction, advancing cell biology research. Furthermore, by combining the high-throughput analysis with our previous finding that a receptor changes its behavioral dynamics depending on the presence of a ligand/agonist or inhibitor/antagonist, we show that AiSIS is applicable to comprehensive pharmacological analysis such as drug screening. This AI-aided automation has wide applications for single-molecule analysis.

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Shien Wee, Ryan Wei, and Andrew MacAskill. "Hippocampal Circuits for the Hunger-Dependent Control of Feeding Behaviour." Journal of the Endocrine Society 5, Supplement_1 (May 1, 2021): A540. http://dx.doi.org/10.1210/jendso/bvab048.1100.

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Abstract Background: Feeding behavior is a complex motivated behavior that requires organisms to integrate features of the environment, such as food availability and value, and internal states, such as hunger, in deliberating over the decision to eat. The hippocampus - a brain region classically thought to support spatial cognition and episodic memory - is increasingly recognised to contribute to such decision-making processes. This function makes the hippocampus a likely candidate in supporting the higher-order decisions that underpin motivated behaviors such as feeding. However, the role of the hippocampus during free-feeding behavior has not been examined. Methods and Results: To address this question, we used in vivo calcium imaging during feeding behavior in mice to monitor the neural activity of the ventral subiculum (vS) - one of the main output structures of the ventral hippocampus. In a free-feeding task, we found that the vS encoded the investigative approach phase of feeding behavior and that activity during this period correlated with the probability of transitioning from food investigation to consumption. Calcium imaging during an operant task confirmed the specific encoding of preparatory behaviour preceding food consumption. Furthermore, the sensitivity of vS to the hunger state could be mapped to vS neurons projecting to the nucleus accumbens (vS-NAc). Ghrelin - a hormone signalling the hunger state - altered synaptic transmission specifically in vS-NAc neurons, and molecular knockdown of the ghrelin receptor was required for the hunger sensitivity of vS-NAc. Consequently, both reducing ghrelin signalling in vS-NAc neurons through molecular knockdown and artificially elevating vS-NAc activity through optogenetics were sufficient to shift the feeding strategy of animals, effectively curtailing overall food consumption. Conclusion: In summary, these results provide evidence for a hippocampal circuit that integrates hunger state signals to regulate the decision to eat.

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Arntsen, B., A. Kritski, B. Ursin, and L. Amundsen. "Shot-profile true amplitude crosscorrelation imaging condition." GEOPHYSICS 78, no. 4 (July 1, 2013): S221—S231. http://dx.doi.org/10.1190/geo2012-0211.1.

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The U/D imaging condition for shot profile migration can be used to estimate the angle dependent reflection coefficient, but is difficult to implement numerically because of the spectral division involved. Most techniques for stabilizing the division require a damping factor which might be difficult to estimate and which also introduces bias into the final result. A stable result can be achieved by approximating the imaging condition with a crosscorrelation of the up- and downgoing wavefields at zero time lag, but this will lead to incorrect amplitude-versus-angle (AVA) behavior of the estimated reflection coefficient. We use a simple model for wave propagation of primary reflections in the wavenumber frequency domain and invert the model with respect to the reflection coefficient. By using the properties of wavefield extrapolators it can then be shown that the reflection coefficients can be estimated by crosscorrelation of the upgoing wavefield and a downgoing wavefield where the initial wavefield is the inverse of the wavefield generated by a point source. The new imaging condition gives the correct AVA behavior for horizontal reflectors. For dipping reflectors it is shown that a postmigration correction factor can be used to recover the correct angle behavior of the reflection coefficient. The new imaging condition is numerically stable, does not involve damping factors, is simple to implement numerically, and is a simple modification of the classical crosscorrelation imaging condition. Numerical examples confirm the correct AVA behavior of the new imaging condition.

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Iversen, Iver H. "RESPONSE-INITIATED IMAGING OF OPERANT BEHAVIOR USING A DIGITAL CAMERA." Journal of the Experimental Analysis of Behavior 77, no. 3 (May 2002): 283–300. http://dx.doi.org/10.1901/jeab.2002.77-283.

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Carpenter, Chris. "Optic Imaging of Two-Phase-Flow Behavior in Nanoscale Fractures." Journal of Petroleum Technology 65, no. 10 (October 1, 2013): 140–43. http://dx.doi.org/10.2118/1013-0140-jpt.

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Couto, Joao, Simon Musall, Xiaonan R. Sun, Anup Khanal, Steven Gluf, Shreya Saxena, Ian Kinsella, et al. "Chronic, cortex-wide imaging of specific cell populations during behavior." Nature Protocols 16, no. 7 (June 2, 2021): 3241–63. http://dx.doi.org/10.1038/s41596-021-00527-z.

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Molenaar, Peter C. M. "Stagewise Development, Behavior Genetics, Brain Imaging, and a “Aha Erlebnis”." International Journal of Developmental Science 6, no. 1-2 (2012): 45–49. http://dx.doi.org/10.3233/dev-2012-11099.

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