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Miura H, Ono Y, Suzuki T, Ogihara Y, Imai Y, Watanabe A, Tokikuni Y, Sakuraba S, Sawamura D. Regional brain activity and neural network changes in cognitive-motor dual-task interference: A functional near-infrared spectroscopy study. Neuroimage 2024; 297:120714. [PMID: 38950665 DOI: 10.1016/j.neuroimage.2024.120714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024] Open
Abstract
Previous neuroimaging studies have reported dual-task interference (DTi) and deterioration of task performance in a cognitive-motor dual task (DT) compared to that in a single task (ST). Greater frontoparietal activity is a neural signature of DTi; nonetheless, the underlying mechanism of cortical network in DTi still remains unclear. This study aimed to investigate the regional brain activity and neural network changes during DTi induced by highly demanding cognitive-motor DT. Thirty-four right-handed healthy young adults performed the spiral-drawing task. They underwent a paced auditory serial addition test (PASAT) simultaneously or independently while their cortical activity was measured using functional near-infrared spectroscopy. Motor performance was determined using the balanced integration score (BIS), a balanced index of drawing speed and precision. The cognitive task of the PASAT was administered with two difficulty levels defined by 1 s (PASAT-1 s) and 2 s (PASAT-2 s) intervals, allowing for the serial addition of numbers. Cognitive performance was determined using the percentage of correct responses. These motor and cognitive performances were significantly reduced during DT, which combined a drawing and a cognitive task at either difficulty level, compared to those in the corresponding ST conditions. The DT conditions were also characterized by significantly increased activity in the right dorsolateral prefrontal cortex (DLPFC) compared to that in the ST conditions. Multivariate Granger causality (GC) analysis of cortical activity in the selected frontoparietal regions of interest further revealed selective top-down causal connectivity from the right DLPFC to the right inferior parietal cortex during DTs. Furthermore, changes in the frontoparietal GC connectivity strength between the PASAT-2 s DT and ST conditions significantly correlated negatively with changes in the percentage of correct responses. Therefore, DTi can occur even in cognitively proficient young adults, and the right DLPFC and frontoparietal network being crucial neural mechanisms underlying DTi. These findings provide new insights into DTi and its underlying neural mechanisms and have implications for the clinical utility of cognitive-motor DTs applied to clinical populations with cognitive decline, such as those with psychiatric and brain disorders.
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Affiliation(s)
- Hiroshi Miura
- Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan; Department of Rehabilitation, Higashinaebo Hospital, Sapporo, Japan
| | - Yumie Ono
- Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University, Kawasaki, Japan
| | - Tatsuya Suzuki
- Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University, Kawasaki, Japan; Electrical Engineering Program, Graduate School of Science and Technology, Meiji University, Kawasaki, Japan
| | - Yuji Ogihara
- Department of Rehabilitation, Higashinaebo Hospital, Sapporo, Japan
| | - Yuna Imai
- Department of Rehabilitation, Higashinaebo Hospital, Sapporo, Japan
| | - Akihiro Watanabe
- Department of Rehabilitation Science, Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, 060-0812, Japan
| | - Yukina Tokikuni
- Department of Rehabilitation Science, Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, 060-0812, Japan
| | - Satoshi Sakuraba
- Department of Rehabilitation Sciences, Health Sciences University of Hokkaido, Tobetsu, Ishikari, Japan
| | - Daisuke Sawamura
- Department of Rehabilitation Science, Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, 060-0812, Japan.
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3
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Ayaz H, Baker WB, Blaney G, Boas DA, Bortfeld H, Brady K, Brake J, Brigadoi S, Buckley EM, Carp SA, Cooper RJ, Cowdrick KR, Culver JP, Dan I, Dehghani H, Devor A, Durduran T, Eggebrecht AT, Emberson LL, Fang Q, Fantini S, Franceschini MA, Fischer JB, Gervain J, Hirsch J, Hong KS, Horstmeyer R, Kainerstorfer JM, Ko TS, Licht DJ, Liebert A, Luke R, Lynch JM, Mesquida J, Mesquita RC, Naseer N, Novi SL, Orihuela-Espina F, O’Sullivan TD, Peterka DS, Pifferi A, Pollonini L, Sassaroli A, Sato JR, Scholkmann F, Spinelli L, Srinivasan VJ, St. Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report. NEUROPHOTONICS 2022; 9:S24001. [PMID: 36052058 PMCID: PMC9424749 DOI: 10.1117/1.nph.9.s2.s24001] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, Pennsylvania, United States
- Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Heather Bortfeld
- University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, California, United States
| | - Kenneth Brady
- Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, Illinois, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - Sabrina Brigadoi
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
| | - Erin M. Buckley
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
| | - Kyle R. Cowdrick
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Joseph P. Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Tokyo, Japan
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Anna Devor
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Turgut Durduran
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain
| | - Adam T. Eggebrecht
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Lauren L. Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Jonas B. Fischer
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Keum-Shik Hong
- Pusan National University, School of Mechanical Engineering, Busan, Republic of Korea
- Qingdao University, School of Automation, Institute for Future, Qingdao, China
| | - Roarke Horstmeyer
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Tiffany S. Ko
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Daniel J. Licht
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Adam Liebert
- Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
| | - Robert Luke
- Macquarie University, Department of Linguistics, Sydney, New South Wales, Australia
- Macquarie University Hearing, Australia Hearing Hub, Sydney, New South Wales, Australia
| | - Jennifer M. Lynch
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Jaume Mesquida
- Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Noman Naseer
- Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
| | - Sergio L. Novi
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | | | - Thomas D. O’Sullivan
- University of Notre Dame, Department of Electrical Engineering, Notre Dame, Indiana, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
| | | | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - João Ricardo Sato
- Federal University of ABC, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
| | - Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Lorenzo Spinelli
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Vivek J. Srinivasan
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- NYU Langone Health, Department of Ophthalmology, New York, New York, United States
- NYU Langone Health, Department of Radiology, New York, New York, United States
| | - Keith St. Lawrence
- Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
- Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Tara Urner
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Heidrun Wabnitz
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Martin Wolf
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Shiqi Xu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Changhuei Yang
- California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wenjun Zhou
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China
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Ji S, Ma H, Yao M, Guo M, Li S, Chen N, Liu X, Shao X, Yao Z, Hu B. Aberrant Temporal Variability in Brain Regions during Risk Decision Making in Patients with Bipolar I Disorder: A Dynamic Effective Connectivity Study. Neuroscience 2021; 469:68-78. [PMID: 34153355 DOI: 10.1016/j.neuroscience.2021.06.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 10/21/2022]
Abstract
Bipolar I disorder (BD-I) is associated with high-risk behaviors, such as suicide attempts and addictive substance abuse. Understanding brain activity exposure to risk decision making provides evidence for the treatment of BD-I patients. This study aimed to investigate the temporal dynamics of brain connectivity underlying risk decision making in patients with BD-I. A total of 101 subjects (48 BD-I patients and 53 age- and gender-matched healthy controls (HCs)) were included in this research. We analyzed the fMRI data acquired during Balloon Analog Risk Task (BART) performance. Voxel-wise dynamic effective connectivity (dEC) was employed to measure the activities in 264 brain regions. The coefficient of variation (CV) was calculated as temporal dynamics of brain connectivity. Finally, we used structural equation modeling (SEM) to determine the relationships of dEC in brain regions with clinical symptoms, behavior performances in patients. Results showed that BD-I patients exhibited increased dynamics in four lobes and exhibited decreased in three frontal regions. Besides, SEM results showed that the impulsive symptoms of patients were affected by the dEC during both resting and task states. Moreover, the dEC of left supramarginal gyrus (SMG) influenced those of left orbital frontal and right cuneus (CUN), as well as the affective symptoms and BART behaviors in patients with BD-I. Our results suggested that the altered temporal dynamics of brain connectivity might contribute to the impulsivity of BD-I during resting and task states. More importantly, the left SMG might be a therapeutic target to reduce the risk behavior in BD-I patients.
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Affiliation(s)
- Shanling Ji
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Hongxia Ma
- School of Clinical Medicine, Jining Medical University, Jining, Shandong Province, China
| | - Mengyuan Yao
- Department of Psychiatry, Jining Psychiatric Hospital, Jining, Shandong Province, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Xia Liu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Xuexiao Shao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; School of Computer Science, Qinghai Normal University, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China.
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7
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Hou X, Zhang Z, Zhao C, Duan L, Gong Y, Li Z, Zhu C. NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis. NEUROPHOTONICS 2021; 8:010802. [PMID: 33506071 PMCID: PMC7829673 DOI: 10.1117/1.nph.8.1.010802] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 01/05/2021] [Indexed: 05/27/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.
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Affiliation(s)
- Xin Hou
- Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Zong Zhang
- Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Chen Zhao
- Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Lian Duan
- Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Yilong Gong
- Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Zheng Li
- Beijing Normal University at Zhuhai, Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Zhuhai, China
- Beijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing, China
| | - Chaozhe Zhu
- Beijing Normal University, IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
- Beijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing, China
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10
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Yang F, Wang Z, Zhang W, Ma H, Cheng Z, Gu Y, Qiu H, Yang S. Wide-field monitoring and real-time local recording of microvascular networks on small animals with a dual-raster-scanned photoacoustic microscope. JOURNAL OF BIOPHOTONICS 2020; 13:e202000022. [PMID: 32101376 DOI: 10.1002/jbio.202000022] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/11/2020] [Accepted: 02/22/2020] [Indexed: 05/18/2023]
Abstract
Photoacoustic microscopy (PAM) provides a new method for the imaging of small-animals with high-contrast and deep-penetration. However, the established PAM systems have suffered from a limited field-of-view or imaging speed, which are difficult to both monitor wide-field activity of organ and record real-time change of local tissue. Here, we reported a dual-raster-scanned photoacoustic microscope (DRS-PAM) that integrates a two-dimensional motorized translation stage for large field-of-view imaging and a two-axis fast galvanometer scanner for real-time imaging. The DRS-PAM provides a flexible transition from wide-field monitoring the vasculature of organs to real-time imaging of local dynamics. To test the performance of DRS-PAM, clear characterization of angiogenesis and functional detail was illustrated, hemodynamic activities of vasculature in cerebral cortex of a mouse were investigated. Furthermore, response of tumor to treatment were successfully monitored during treatment. The experimental results demonstrate the DRS-PAM holds the great potential for biomedical research of basic biology.
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Affiliation(s)
- Fei Yang
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Zhiyang Wang
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Wuyu Zhang
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Haigang Ma
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Zhongwen Cheng
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
| | - Ying Gu
- Department of Laser Medicine, First Medical Center of PLA General Hospital, Beijing, China
| | - Haixia Qiu
- Department of Laser Medicine, First Medical Center of PLA General Hospital, Beijing, China
| | - Sihua Yang
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou, China
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