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Gao Y, Rogers D, von Lühmann A, Ortega-Martinez A, Boas DA, Yücel MA. Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy. NEUROPHOTONICS 2023; 10:025007. [PMID: 37228904 PMCID: PMC10203730 DOI: 10.1117/1.nph.10.2.025007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 04/08/2023] [Accepted: 05/03/2023] [Indexed: 05/27/2023]
Abstract
Significance Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance. Aim Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously. Approach The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT. Results The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation. Conclusions The SS-DOT model improves the fNIRS image reconstruction quality.
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Affiliation(s)
- Yuanyuan Gao
- Boston University, Neurophotonics Center, Boston, Massachusetts, United States
| | - De’Ja Rogers
- Boston University, Neurophotonics Center, Boston, Massachusetts, United States
| | | | | | - David A. Boas
- Boston University, Neurophotonics Center, Boston, Massachusetts, United States
| | - Meryem Ayşe Yücel
- Boston University, Neurophotonics Center, Boston, Massachusetts, United States
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Su WC, Dashtestani H, Miguel HO, Condy E, Buckley A, Park S, Perreault JB, Nguyen T, Zeytinoglu S, Millerhagen J, Fox N, Gandjbakhche A. Simultaneous multimodal fNIRS-EEG recordings reveal new insights in neural activity during motor execution, observation, and imagery. Sci Rep 2023; 13:5151. [PMID: 36991003 PMCID: PMC10060581 DOI: 10.1038/s41598-023-31609-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
Motor execution, observation, and imagery are important skills used in motor learning and rehabilitation. The neural mechanisms underlying these cognitive-motor processes are still poorly understood. We used a simultaneous recording of functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) to elucidate the differences in neural activity across three conditions requiring these processes. Additionally, we used a new method called structured sparse multiset Canonical Correlation Analysis (ssmCCA) to fuse the fNIRS and EEG data and determine the brain regions of neural activity consistently detected by both modalities. Unimodal analyses revealed differentiated activation between conditions; however, the activated regions did not fully overlap across the two modalities (fNIRS: left angular gyrus, right supramarginal gyrus, as well as right superior and inferior parietal lobes; EEG: bilateral central, right frontal, and parietal). These discrepancies might be because fNIRS and EEG detect different signals. Using fused fNIRS-EEG data, we consistently found activation over the left inferior parietal lobe, superior marginal gyrus, and post-central gyrus during all three conditions, suggesting that our multimodal approach identifies a shared neural region associated with the Action Observation Network (AON). This study highlights the strengths of using the multimodal fNIRS-EEG fusion technique for studying AON. Neural researchers should consider using the multimodal approach to validate their findings.
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Affiliation(s)
- Wan-Chun Su
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Hadis Dashtestani
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Helga O Miguel
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Emma Condy
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Aaron Buckley
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Soongho Park
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - John B Perreault
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Thien Nguyen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Selin Zeytinoglu
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - John Millerhagen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA
| | - Nathan Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Amir Gandjbakhche
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, USA.
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Ortega-Martinez A, Rogers D, Anderson J, Farzam P, Gao Y, Zimmermann B, Yücel MA, Boas DA. How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS? NEUROPHOTONICS 2023; 10:013504. [PMID: 36284602 PMCID: PMC9587749 DOI: 10.1117/1.nph.10.1.013504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
SIGNIFICANCE Advances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF. AIMS We compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM. APPROACH We augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with "short-separation regression" designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests. RESULTS We found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment. CONCLUSIONS Properly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.
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Affiliation(s)
| | - De’Ja Rogers
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Jessica Anderson
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Parya Farzam
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Yuanyuan Gao
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Bernhard Zimmermann
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
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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: 42] [Impact Index Per Article: 21.0] [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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Mozumder M, Hauptmann A, Nissila I, Arridge SR, Tarvainen T. A Model-Based Iterative Learning Approach for Diffuse Optical Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1289-1299. [PMID: 34914584 DOI: 10.1109/tmi.2021.3136461] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a 'model-based' learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration.
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Molina-Rodríguez S, Mirete-Fructuoso M, Martínez LM, Ibañez-Ballesteros J. Frequency-domain analysis of fNIRS fluctuations induced by rhythmic mental arithmetic. Psychophysiology 2022; 59:e14063. [PMID: 35394075 PMCID: PMC9540762 DOI: 10.1111/psyp.14063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 01/19/2022] [Accepted: 03/08/2022] [Indexed: 12/25/2022]
Abstract
Functional near‐infrared spectroscopy (fNIRS) is an increasingly used technology for imaging neural correlates of cognitive processes. However, fNIRS signals are commonly impaired by task‐evoked and spontaneous hemodynamic oscillations of non‐cerebral origin, a major challenge in fNIRS research. In an attempt to isolate the task‐evoked cortical response, we investigated the coupling between hemodynamic changes arising from superficial and deep layers during mental effort. For this aim, we applied a rhythmic mental arithmetic task to induce cyclic hemodynamic fluctuations suitable for effective frequency‐resolved measurements. Twenty university students aged 18–25 years (eight males) underwent the task while hemodynamic changes were monitored in the forehead using a newly developed NIRS device, capable of multi‐channel and multi‐distance recordings. We found significant task‐related fluctuations for oxy‐ and deoxy‐hemoglobin, highly coherent across shallow and deep tissue layers, corroborating the strong influence of surface hemodynamics on deep fNIRS signals. Importantly, after removing such surface contamination by linear regression, we show that the frontopolar cortex response to a mental math task follows an unusual inverse oxygenation pattern. We confirm this finding by applying for the first time an alternative method to estimate the neural signal, based on transfer function analysis and phasor algebra. Altogether, our results demonstrate the feasibility of using a rhythmic mental task to impose an oscillatory state useful to separate true brain functional responses from those of non‐cerebral origin. This separation appears to be essential for a better understanding of fNIRS data and to assess more precisely the dynamics of the neuro‐visceral link. We proposed the use of rhythmic mental arithmetic tasks to induce cyclic oscillations in multi‐distance fNIRS signals measured on the forehead, suitable for effective frequency‐domain analysis to better identify the actual neural functional response. We confirm the impairment of deep signals by task‐evoked non‐cerebral confounds, while providing evidence for an inverse oxygenation response in the frontopolar cortex.
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Affiliation(s)
- Sergio Molina-Rodríguez
- Cellular and Systems Neurobiology, Institute of Neurosciences, Spanish National Research Council-Miguel Hernandez University, Alicante, Spain
| | - Marcos Mirete-Fructuoso
- Cellular and Systems Neurobiology, Institute of Neurosciences, Spanish National Research Council-Miguel Hernandez University, Alicante, Spain
| | - Luis M Martínez
- Cellular and Systems Neurobiology, Institute of Neurosciences, Spanish National Research Council-Miguel Hernandez University, Alicante, Spain
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7
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Ortega-Martinez A, Von Lühmann A, Farzam P, Rogers D, Mugler EM, Boas DA, Yücel MA. Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data. NEUROPHOTONICS 2022; 9:025003. [PMID: 35692628 PMCID: PMC9174890 DOI: 10.1117/1.nph.9.2.025003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 05/17/2022] [Indexed: 05/13/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain-computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.
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Affiliation(s)
| | - Alexander Von Lühmann
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Berlin Institute of Technology, Machine Learning Department, Berlin, Germany
| | - Parya Farzam
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - De’Ja Rogers
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Emily M. Mugler
- Facebook Reality Labs Research, Menlo Park, California, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
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8
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Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli. Sci Rep 2021; 11:23457. [PMID: 34873185 PMCID: PMC8648757 DOI: 10.1038/s41598-021-02076-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/18/2021] [Indexed: 11/08/2022] Open
Abstract
Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within the VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.
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9
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Gilmore N, Yücel MA, Li X, Boas DA, Kiran S. Investigating Language and Domain-General Processing in Neurotypicals and Individuals With Aphasia - A Functional Near-Infrared Spectroscopy Pilot Study. Front Hum Neurosci 2021; 15:728151. [PMID: 34602997 PMCID: PMC8484538 DOI: 10.3389/fnhum.2021.728151] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/25/2021] [Indexed: 11/29/2022] Open
Abstract
Brain reorganization patterns associated with language recovery after stroke have long been debated. Studying mechanisms of spontaneous and treatment-induced language recovery in post-stroke aphasia requires a network-based approach given the potential for recruitment of perilesional left hemisphere language regions, homologous right hemisphere language regions, and/or spared bilateral domain-general regions. Recent hardware, software, and methodological advances in functional near-infrared spectroscopy (fNIRS) make it well-suited to examine this question. fNIRS is cost-effective with minimal contraindications, making it a robust option to monitor treatment-related brain activation changes over time. Establishing clear activation patterns in neurotypical adults during language and domain-general cognitive processes via fNIRS is an important first step. Some fNIRS studies have investigated key language processes in healthy adults, yet findings are challenging to interpret in the context of methodological limitations. This pilot study used fNIRS to capture brain activation during language and domain-general processing in neurotypicals and individuals with aphasia. These findings will serve as a reference when interpreting treatment-related changes in brain activation patterns in post-stroke aphasia in the future. Twenty-four young healthy controls, seventeen older healthy controls, and six individuals with left hemisphere stroke-induced aphasia completed two language tasks (i.e., semantic feature, picture naming) and one domain-general cognitive task (i.e., arithmetic) twice during fNIRS. The probe covered bilateral frontal, parietal, and temporal lobes and included short-separation detectors for scalp signal nuisance regression. Younger and older healthy controls activated core language regions during semantic feature processing (e.g., left inferior frontal gyrus pars opercularis) and lexical retrieval (e.g., left inferior frontal gyrus pars triangularis) and domain-general regions (e.g., bilateral middle frontal gyri) during hard versus easy arithmetic as expected. Consistent with theories of post-stroke language recovery, individuals with aphasia activated areas outside the traditional networks: left superior frontal gyrus and left supramarginal gyrus during semantic feature judgment; left superior frontal gyrus and right precentral gyrus during picture naming; and left inferior frontal gyrus pars opercularis during arithmetic processing. The preliminary findings in the stroke group highlight the utility of using fNIRS to study language and domain-general processing in aphasia.
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Affiliation(s)
- Natalie Gilmore
- Department of Speech Language & Hearing Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States
| | - Meryem Ayse Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Xinge Li
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States.,Department of Psychology, College of Liberal Arts and Social Sciences, University of Houston, Houston, TX, United States
| | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Swathi Kiran
- Department of Speech Language & Hearing Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States
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10
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Dans PW, Foglia SD, Nelson AJ. Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research. Brain Sci 2021; 11:606. [PMID: 34065136 PMCID: PMC8151801 DOI: 10.3390/brainsci11050606] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/05/2021] [Accepted: 05/07/2021] [Indexed: 12/26/2022] Open
Abstract
FNIRS pre-processing and processing methodologies are very important-how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.
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Affiliation(s)
- Patrick W. Dans
- Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada;
| | - Stevie D. Foglia
- School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada;
| | - Aimee J. Nelson
- Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada;
- School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada;
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11
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Hernandez-Martin E, Gonzalez-Mora JL. Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2020.9050014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
The present work describes the use of noninvasive diffuse optical tomography (DOT) technology to measure hemodynamic changes, providing relevant information which helps to understand the basis of neurophysiology in the human brain. Advantages such as portability, direct measurements of hemoglobin state, temporal resolution, non‐restricted movements as occurs in magnetic resonance imaging (MRI) devices mean that DOT technology can be used in research and clinical fields. In this review we covered the neurophysiology, physical principles underlying optical imaging during tissue‐light interactions, and technology commonly used during the construction of a DOT device including the source‐detector requirements to improve the image quality. DOT provides 3D cerebral activation images due to complex mathematical models which describe the light propagation inside the tissue head. Moreover, we describe briefly the use of Bayesian methods for raw DOT data filtering as an alternative to linear filters widely used in signal processing, avoiding common problems such as the filter selection or a false interpretation of the results which is sometimes due to the interference of background physiological noise with neural activity.
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Affiliation(s)
- Estefania Hernandez-Martin
- Department of Basic Medical Science, Faculty of Health Science, Medicine Section, Universidad de La Laguna, 38071, Spain
| | - José Luis Gonzalez-Mora
- Department of Basic Medical Science, Faculty of Health Science, Medicine Section, Universidad de La Laguna, 38071, Spain
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12
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Santosa H, Zhai X, Fishburn F, Sparto PJ, Huppert TJ. Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies. NEUROPHOTONICS 2020; 7:035009. [PMID: 32995361 PMCID: PMC7511246 DOI: 10.1117/1.nph.7.3.035009] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/27/2020] [Indexed: 05/15/2023]
Abstract
Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic "brain" responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.
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Affiliation(s)
- Hendrik Santosa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Xuetong Zhai
- University of Pittsburgh, Department of Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Frank Fishburn
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, Pennsylvania, United States
| | - Patrick J. Sparto
- University of Pittsburgh, Department of Physical Therapy, Pittsburgh, Pennsylvania, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Clinical Science Translational Institute, Pittsburgh, Pennsylvania, United States
- University of Pittsburgh, Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States
- University of Pittsburgh, Department of Electrical and Computer Engineering, Pittsburgh, Pennsylvania, United States
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13
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Yan W, Zheng K, Weng L, Chen C, Kiartivich S, Jiang X, Su X, Wang Y, Wang X. Bibliometric evaluation of 2000-2019 publications on functional near-infrared spectroscopy. Neuroimage 2020; 220:117121. [PMID: 32619709 DOI: 10.1016/j.neuroimage.2020.117121] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 06/23/2020] [Accepted: 06/27/2020] [Indexed: 02/07/2023] Open
Abstract
This study aimed to explore and analyze research trends and frontiers on functional near-infrared spectroscopy (fNIRS) in the past 20 years and identify collaboration networks. fNIRS-related publications from 2000 to 2019 were retrieved from the Web of Science database. A total of 1727 publications satisfied the search criteria. Bibliometric visualization analysis of active authors, journals, institutions, countries, references, and keywords were conducted. The number of annual related publications remarkably increased over the years. Fallgatter published the largest number of fNIRS-related papers (83). Neuroimage not only had the largest number of papers published in the first 10 journals (157 articles) but also had the highest impact factor (IF, 2018 = 5.812). The University of Tubingen had the highest number of fNIRS-related publications in the past 20 years. The United States ranked first in terms of comprehensive influence in this field. In recent years, burst keywords (e.g., infant, social interaction, and older adult) and a series of references with citation burst provided clues on research frontiers.
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Affiliation(s)
- Wangwang Yan
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China; Department of Rehabilitation Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kangyong Zheng
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Linman Weng
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Changcheng Chen
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Suparata Kiartivich
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Xue Jiang
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China; Department of Rehabilitation Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xuan Su
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Yuling Wang
- Department of Rehabilitation Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
| | - Xueqiang Wang
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China; Department of Rehabilitation Medicine, Shanghai Shangti Orthopaedic Hospital, Shanghai, China.
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14
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Wang B, Zhang Y, Liu D, Pan T, Liu Y, Bai L, Zhou Z, Jiang J, Gao F. Joint direct estimation of hemodynamic response function and activation level in brain functional high density diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2020; 11:3025-3042. [PMID: 32637239 PMCID: PMC7316018 DOI: 10.1364/boe.386567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/31/2020] [Accepted: 04/25/2020] [Indexed: 06/11/2023]
Abstract
High density diffuse optical tomography has become increasingly important to detect underlying neuronal activities. Conventional methods first estimate the time courses of the changes in the absorption coefficients for all the voxels, and then estimate the hemodynamic response function (HRF). Activation-level maps are extracted at last based on this HRF. However, the error propagation among the successive processes degrades and even misleads the final results. Besides, the computation burden is heavy. To address the above problems, a direct method is proposed in this paper to simultaneously estimate the HRF and the activation-level maps from the boundary fluxes. It is assumed that all the voxels in the same activated brain region share the same HRF but differ in the activation levels, and no prior information is imposed on the specific shape of the HRF. The dynamic simulation and phantom experiments demonstrate that the proposed method outperforms the conventional one in terms of the estimation accuracy and computation speed.
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Affiliation(s)
- Bingyuan Wang
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
| | - Yao Zhang
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
| | - Dongyuan Liu
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
| | - Tiantian Pan
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
| | - Yang Liu
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
| | - Lu Bai
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
| | - Zhongxing Zhou
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
- Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, No. 92 Weijin Road, Tianjin, China, 300072
| | - Jingying Jiang
- Beihang University, Beijing Advanced Innovation Center for Big Data-based Precision Medicine, No. 37 Xueyuan Road, Beijing, China, 100191
| | - Feng Gao
- Tianjin University, College of Precision Instrument and Optoelectronics Engineering, No. 92 Weijin Road, Tianjin, China, 300072
- Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, No. 92 Weijin Road, Tianjin, China, 300072
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15
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von Lühmann A, Ortega-Martinez A, Boas DA, Yücel MA. Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Front Hum Neurosci 2020; 14:30. [PMID: 32132909 PMCID: PMC7040364 DOI: 10.3389/fnhum.2020.00030] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 01/22/2020] [Indexed: 11/28/2022] Open
Abstract
Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.
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Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States.,Machine Learning Department, Berlin Institute of Technology, Berlin, Germany
| | | | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Meryem Ayşe Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
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16
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Li R, Zhao C, Wang C, Wang J, Zhang Y. Enhancing fNIRS Analysis Using EEG Rhythmic Signatures: An EEG-Informed fNIRS Analysis Study. IEEE Trans Biomed Eng 2020; 67:2789-2797. [PMID: 32031925 DOI: 10.1109/tbme.2020.2971679] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Neurovascular coupling represents the relationship between changes in neuronal activity and cerebral hemodynamics. Concurrent Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording and integration analysis has emerged as a promising multi-modal neuroimaging approach to study the neurovascular coupling as it provides complementary properties with regard to high temporal and moderate spatial resolution of brain activity. In this study we developed an EEG-informed-fNIRS analysis framework to investigate the neuro-correlate between neuronal activity and cerebral hemodynamics by identifying specific EEG rhythmic modulations which contribute to the improvement of the fNIRS-based general linear model (GLM) analysis. Specifically, frequency-specific regressors derived from EEG were used to construct design matrices to guide the GLM analysis of the fNIRS signals collected during a hand grasp task. Our results showed that the EEG-informed fNIRS GLM analysis, especially the alpha and beta band, revealed significantly higher sensitivity and specificity in localizing the task-evoked regions compared to the canonical boxcar model, demonstrating the strong correlations between hemodynamic response and EEG rhythmic modulations. Results also indicated that analysis based on the deoxygenated hemoglobin (HbR) signal slightly outperformed the oxygenated hemoglobin (HbO)-based analysis. The findings in our study not only validate the feasibility of enhancing fNIRS GLM analysis using simultaneously recorded EEG signals, but also provide a new perspective to study the neurovascular coupling of brain activity.
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17
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von Lühmann A, Li X, Müller KR, Boas DA, Yücel MA. Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis. Neuroimage 2019; 208:116472. [PMID: 31870944 PMCID: PMC7703677 DOI: 10.1016/j.neuroimage.2019.116472] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/04/2019] [Accepted: 12/17/2019] [Indexed: 01/28/2023] Open
Abstract
For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. −55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.
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Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA; Machine Learning Department, Berlin Institute of Technology, 10587, Berlin, Germany.
| | - Xinge Li
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Klaus-Robert Müller
- Machine Learning Department, Berlin Institute of Technology, 10587, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, South Korea; Max Planck Institute for Informatics, Saarbrücken, 66123, Germany
| | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Meryem A Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
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18
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Modi HN, Singh H, Fiorentino F, Orihuela-Espina F, Athanasiou T, Yang GZ, Darzi A, Leff DR. Association of Residents' Neural Signatures With Stress Resilience During Surgery. JAMA Surg 2019; 154:e192552. [PMID: 31389994 DOI: 10.1001/jamasurg.2019.2552] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Importance Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC). Objective To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. Design, Setting, and Participants Cohort study conducted from July 2015 to September 2016 at the Imperial College Healthcare National Health Service Trust, England. One hundred two surgical residents (postgraduate year 1 and greater) were invited to participate, of which 33 agreed to partake. Exposures Participants performed a laparoscopic suturing task under 2 conditions: self-paced (SP; without time-per-knot restrictions), and time pressure (TP; 2-minute per knot time restriction). Main Outcomes and Measures A composite deterioration score was computed based on between-condition differences in task performance metrics (task progression score [arbitrary units], error score [millimeters], leak volume [milliliters], and knot tensile strength [newtons]). Based on the composite score, quartiles were computed reflecting performance stability (quartile 1 [Q1]) and decline (quartile 4 [Q4]). Changes in PFC oxygenated hemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results Of the 33 participants, the median age was 33 years, the range was 29 to 56 years, and 27 were men (82%). The Q1 residents demonstrated task-induced increases in HbO2 across the bilateral ventrolateral PFC (VLPFC) and right dorsolateral PFC in the SP condition and in the VLPFC in the TP condition. In contrast, Q4 residents demonstrated decreases in HbO2 in both conditions. The magnitude of PFC activation (change in HbO2) was significantly greater in Q1 than Q4 across the bilateral VLPFC during both SP (mean [SD] left VLPFC: Q1, 0.44 [1.30] μM; Q4, -0.21 [2.05] μM; P < .001; right VLPFC: Q1, 0.46 [1.12] μM; Q4, -0.15 [2.14] μM; P < .001) and TP (mean [SD] left VLPFC: Q1, 0.44 [1.36] μM; Q4, -0.03 [1.83] μM; P = .001; right VLPFC: Q1, 0.49 [1.70] μM; Q4, -0.32 [2.00] μM; P < .001) conditions. There were no significant between-group differences in Surgical Task Load Index or heart rate in either condition. Conclusions and Relevance Performance stability within TP is associated with sustained prefrontal activation indicative of preserved attention and concentration, whereas performance decline is associated with prefrontal deactivation that may represent task disengagement.
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Affiliation(s)
| | - Harsimrat Singh
- Department of Surgery and Cancer, Imperial College London, London, England
| | | | | | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, England
| | - Guang-Zhong Yang
- Hamlyn Centre for Robotic Surgery, Imperial College London, London, England
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, London, England.,Hamlyn Centre for Robotic Surgery, Imperial College London, London, England
| | - Daniel Richard Leff
- Department of Surgery and Cancer, Imperial College London, London, England.,Hamlyn Centre for Robotic Surgery, Imperial College London, London, England
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19
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Li B, Esipova TV, Sencan I, Kılıç K, Fu B, Desjardins M, Moeini M, Kura S, Yaseen MA, Lesage F, Østergaard L, Devor A, Boas DA, Vinogradov SA, Sakadžić S. More homogeneous capillary flow and oxygenation in deeper cortical layers correlate with increased oxygen extraction. eLife 2019; 8:42299. [PMID: 31305237 PMCID: PMC6636997 DOI: 10.7554/elife.42299] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 07/01/2019] [Indexed: 01/01/2023] Open
Abstract
Our understanding of how capillary blood flow and oxygen distribute across cortical layers to meet the local metabolic demand is incomplete. We addressed this question by using two-photon imaging of resting-state microvascular oxygen partial pressure (PO2) and flow in the whisker barrel cortex in awake mice. Our measurements in layers I-V show that the capillary red-blood-cell flux and oxygenation heterogeneity, and the intracapillary resistance to oxygen delivery, all decrease with depth, reaching a minimum around layer IV, while the depth-dependent oxygen extraction fraction is increased in layer IV, where oxygen demand is presumably the highest. Our findings suggest that more homogeneous distribution of the physiological observables relevant to oxygen transport to tissue is an important part of the microvascular network adaptation to local brain metabolism. These results will inform the biophysical models of layer-specific cerebral oxygen delivery and consumption and improve our understanding of the diseases that affect cerebral microcirculation.
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Affiliation(s)
- Baoqiang Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
| | - Tatiana V Esipova
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, United States.,Department of Chemistry, University of Pennsylvania, Philadelphia, United States
| | - Ikbal Sencan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
| | - Kıvılcım Kılıç
- Department of Neurosciences, University of California, San Diego, La Jolla, United States
| | - Buyin Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
| | - Michele Desjardins
- Department of Radiology, University of California, San Diego, La Jolla, United States
| | - Mohammad Moeini
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Canada.,Research Centre, Montreal Heart Institute, Montréal, Canada
| | - Sreekanth Kura
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
| | - Mohammad A Yaseen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
| | - Frederic Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Canada.,Research Centre, Montreal Heart Institute, Montréal, Canada
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Anna Devor
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States.,Department of Neurosciences, University of California, San Diego, La Jolla, United States.,Department of Radiology, University of California, San Diego, La Jolla, United States
| | - David A Boas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States.,Department of Biomedical Engineering, Boston University, Boston, United States
| | - Sergei A Vinogradov
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, United States.,Department of Chemistry, University of Pennsylvania, Philadelphia, United States
| | - Sava Sakadžić
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
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20
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Jahani S, Setarehdan SK, Boas DA, Yücel MA. Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering. NEUROPHOTONICS 2018; 5:015003. [PMID: 29430471 PMCID: PMC5803523 DOI: 10.1117/1.nph.5.1.015003] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/04/2018] [Indexed: 05/06/2023]
Abstract
Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important challenge in realizing the full potential of NIRS for real-life applications. Various motion correction algorithms have been used to alleviate the effect of motion artifacts on the estimation of the hemodynamic response function. While smoothing methods, such as wavelet filtering, are excellent in removing motion-induced sharp spikes, the baseline shifts in the signal remain after this type of filtering. Methods, such as spline interpolation, on the other hand, can properly correct baseline shifts; however, they leave residual high-frequency spikes. We propose a hybrid method that takes advantage of different correction algorithms. This method first identifies the baseline shifts and corrects them using a spline interpolation method or targeted principal component analysis. The remaining spikes, on the other hand, are corrected by smoothing methods: Savitzky-Golay (SG) filtering or robust locally weighted regression and smoothing. We have compared our new approach with the existing correction algorithms in terms of hemodynamic response function estimation using the following metrics: mean-squared error, peak-to-peak error ([Formula: see text]), Pearson's correlation ([Formula: see text]), and the area under the receiver operator characteristic curve. We found that spline-SG hybrid method provides reasonable improvements in all these metrics with a relatively short computational time. The dataset and the code used in this study are made available online for the use of all interested researchers.
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Affiliation(s)
- Sahar Jahani
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States
- University of Tehran, College of Engineering, School of Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, Tehran, Iran
| | - Seyed K. Setarehdan
- University of Tehran, College of Engineering, School of Electrical and Computer Engineering, Control and Intelligent Processing Center of Excellence, Tehran, Iran
| | - David A. Boas
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
| | - Meryem A. Yücel
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States
- Address all correspondence to: Meryem A. Yücel, E-mail:
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21
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Lu FM, Wang YF, Zhang J, Chen HF, Yuan Z. Optical mapping of the dominant frequency of brain signal oscillations in motor systems. Sci Rep 2017; 7:14703. [PMID: 29116158 PMCID: PMC5677051 DOI: 10.1038/s41598-017-15046-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 10/17/2017] [Indexed: 12/17/2022] Open
Abstract
Recent neuroimaging studies revealed that the dominant frequency of neural oscillations is brain-region-specific and can vary with frequency-specific reorganization of brain networks during cognition. In this study, we examined the dominant frequency in low-frequency neural oscillations represented by oxygenated hemoglobin measurements after the hemodynamic response function (HRF) deconvolution. Twenty-nine healthy college subjects were recruited to perform a serial finger tapping task at the frequency of 0.2 Hz. Functional near-infrared spectroscopy (fNIRS) was applied to record the hemodynamic signals over the primary motor cortex, supplementary motor area (SMA), premotor cortex, and prefrontal area. We then explored the low frequency steady-state brain response (lfSSBR), which was evoked in the motor systems at the fundamental frequency (0.2 Hz) and its harmonics (0.4, 0.6, and 0.8 Hz). In particular, after HRF deconvolution, the lfSSBR at the frequency of 0.4 Hz in the SMA was identified as the dominant frequency. Interestingly, the domain frequency exhibited the correlation with behavior data such as reaction time, indicating that the physiological implication of lfSSBR is related to the brain anatomy, stimulus frequency and cognition. More importantly, the HRF deconvolution showed its capability for recovering signals probably reflecting neural-level events and revealing the physiological meaning of lfSSBR.
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Affiliation(s)
- Feng-Mei Lu
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, SAR, China
| | - Yi-Feng Wang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Juan Zhang
- Faculty of Education, University of Macau, Macau, SAR, China
| | - Hua-Fu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zhen Yuan
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, SAR, China.
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22
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fNIRS can robustly measure brain activity during memory encoding and retrieval in healthy subjects. Sci Rep 2017; 7:9533. [PMID: 28842618 PMCID: PMC5572719 DOI: 10.1038/s41598-017-09868-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 07/31/2017] [Indexed: 11/17/2022] Open
Abstract
Early intervention in Alzheimer’s Disease (AD) requires novel biomarkers that can capture changes in brain activity at an early stage. Current AD biomarkers are expensive and/or invasive and therefore unsuitable for use as screening tools, but a non-invasive, inexpensive, easily accessible screening method could be useful in both clinical and research settings. Prior studies suggest that especially paired-associate learning tasks may be useful in detecting the earliest memory impairment in AD. Here, we investigated the utility of functional Near Infrared Spectroscopy in measuring brain activity from prefrontal, parietal and temporal cortices of healthy adults (n = 19) during memory encoding and retrieval under a face-name paired-associate learning task. Our findings demonstrate that encoding of novel face-name pairs compared to baseline as well as compared to repeated face-name pairs resulted in significant activation in left dorsolateral prefrontal cortex while recalling resulted in activation in dorsolateral prefrontal cortex bilaterally. Moreover, brain response to recalling was significantly higher than encoding in medial, superior and middle frontal cortices for novel faces. Overall, this study shows that fNIRS can reliably measure cortical brain activation during a face-name paired-associate learning task. Future work will include similar measurements in populations with progressing memory deficits.
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23
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Santosa H, Aarabi A, Perlman SB, Huppert TJ. Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:55002. [PMID: 28492852 PMCID: PMC5424771 DOI: 10.1117/1.jbo.22.5.055002] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 04/11/2017] [Indexed: 05/18/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of red to near-infrared light to measure changes in cerebral blood oxygenation. Spontaneous (resting state) functional connectivity (sFC) has become a critical tool for cognitive neuroscience for understanding task-independent neural networks, revealing pertinent details differentiating healthy from disordered brain function, and discovering fluctuations in the synchronization of interacting individuals during hyperscanning paradigms. Two of the main challenges to sFC-NIRS analysis are (i) the slow temporal structure of both systemic physiology and the response of blood vessels, which introduces false spurious correlations, and (ii) motion-related artifacts that result from movement of the fNIRS sensors on the participants’ head and can introduce non-normal and heavy-tailed noise structures. In this work, we systematically examine the false-discovery rates of several time- and frequency-domain metrics of functional connectivity for characterizing sFC-NIRS. Specifically, we detail the modifications to the statistical models of these methods needed to avoid high levels of false-discovery related to these two sources of noise in fNIRS. We compare these analysis procedures using both simulated and experimental resting-state fNIRS data. Our proposed robust correlation method has better performance in terms of being more reliable to the noise outliers due to the motion artifacts.
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Affiliation(s)
- Hendrik Santosa
- University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States
| | - Ardalan Aarabi
- Universite de Picardie Jules Verne, Department of Medicine, Amiens, France
| | - Susan B. Perlman
- University of Pittsburgh, Department of Psychiatry, Pittsburgh, Pennsylvania, United States
| | - Theodore J. Huppert
- University of Pittsburgh, Departments of Radiology and Bioengineering, Clinical Science Translational Institute, and Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States
- Address all correspondence to: Theodore J. Huppert, E-mail:
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24
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Aarabi A, Osharina V, Wallois F. Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution analysis: An event-related NIRS study. Neuroimage 2017; 155:25-49. [PMID: 28450140 DOI: 10.1016/j.neuroimage.2017.04.048] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 04/12/2017] [Accepted: 04/20/2017] [Indexed: 11/17/2022] Open
Abstract
Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs.
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Affiliation(s)
- Ardalan Aarabi
- Faculty of Medicine, University of Picardie Jules Verne, Amiens 80036, France; GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France.
| | - Victoria Osharina
- GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France
| | - Fabrice Wallois
- GRAMFC-Inserm U1105, University Research Center (CURS), University Hospital, Amiens, 80054 France; EFSN Pediatric (Pediatric Nervous System Functional Investigation Unit), CHU AMIENS - SITE SUD, Amiens, France
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25
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Brigadoi S, Phan P, Highton D, Powell S, Cooper RJ, Hebden J, Smith M, Tachtsidis I, Elwell CE, Gibson AP. Image reconstruction of oxidized cerebral cytochrome C oxidase changes from broadband near-infrared spectroscopy data. NEUROPHOTONICS 2017; 4:021105. [PMID: 28560239 PMCID: PMC5443419 DOI: 10.1117/1.nph.4.2.021105] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 05/01/2017] [Indexed: 05/23/2023]
Abstract
In diffuse optical tomography (DOT), overlapping and multidistance measurements are required to reconstruct depth-resolved images of oxy- ([Formula: see text]) and deoxy- (HHb) hemoglobin concentration changes occurring in the brain. These can be considered an indirect measure of brain activity, under the assumption of intact neurovascular coupling. Broadband systems also allow changes in the redox state of cytochrome c oxidase (oxCCO) to be measured, which can be an important biomarker when neurovascular coupling is impaired. We used DOT to reconstruct images of [Formula: see text], [Formula: see text], and [Formula: see text] from data acquired with a broadband system. Four healthy volunteers were measured while performing a visual stimulation task (4-Hz inverting checkerboard). The broadband system was configured to allow multidistance and overlapping measurements of the participants' visual cortex with 32 channels. A multispectral approach was employed to reconstruct changes in concentration of the three chromophores during the visual stimulation. A clear and focused activation was reconstructed in the left occipital cortex of all participants. The difference between the residuals of the three-chromophore model and of the two-chromophore model (recovering only [Formula: see text] and [Formula: see text]) exhibits a spectrum similar to that of oxCCO. These results form a basis for further studies aimed to further optimize image reconstruction of [Formula: see text].
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Affiliation(s)
- Sabrina Brigadoi
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
- University of Padova, Department of Developmental and Social Psychology, Padova, Italy
| | - Phong Phan
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - David Highton
- National Hospital for Neurology and Neurosurgery, Neurocritical Care, London, United Kingdom
| | - Samuel Powell
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
- University College London, Department of Computer Science, London, United Kingdom
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Jeremy Hebden
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Martin Smith
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
- National Hospital for Neurology and Neurosurgery, Neurocritical Care, London, United Kingdom
- NIHR University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Clare E. Elwell
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Adam P. Gibson
- University College London, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
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26
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Liang Z, Gu Y, Duan X, Cheng L, Liang S, Tong Y, Li X. Design of multichannel functional near-infrared spectroscopy system with application to propofol and sevoflurane anesthesia monitoring. NEUROPHOTONICS 2016; 3:045001. [PMID: 27725946 PMCID: PMC5050277 DOI: 10.1117/1.nph.3.4.045001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 09/12/2016] [Indexed: 05/30/2023]
Abstract
Monitoring the changes of cerebral hemodynamics and the state of consciousness during general anesthesia (GA) is clinically important. There is a great need for developing advanced detectors to investigate the physiological processes of the brain during GA. We developed a multichanneled, functional near-infrared spectroscopy (fNIRS) system device and applied it to GA operation monitoring. The cerebral hemodynamic data from the forehead of 11 patients undergoing propofol and sevoflurane anesthesia were analyzed. The concentration changes of oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin, and cerebral tissue heart rate were determined from the raw optical information based on the discrete stationary wavelet transform. This custom-made device provides an easy-to-build solution for continuous wave-fNIRS system, with customized specifications. The developed device has a potential value in cerebral monitoring in clinical settings.
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Affiliation(s)
- Zhenhu Liang
- Yanshan University, Institute of Electrical Engineering, No. 438 Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Yue Gu
- Yanshan University, Institute of Electrical Engineering, No. 438 Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Xuejing Duan
- Yanshan University, Institute of Electrical Engineering, No. 438 Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Lei Cheng
- Yanshan University, Institute of Electrical Engineering, No. 438 Hebei Street, Haigang District, Qinhuangdao 066004, China
| | - Shujuan Liang
- Department of Anesthesia, No. 1 Hospital of Qinhuangdao, No. 258 Wenhua Street, Haigang District, Qinhuangdao 066004, China
| | - Yunjie Tong
- McLean Hospital, McLean Imaging Center, 115 Mill Street, Belmont, Massachusetts 02478, United States
| | - Xiaoli Li
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, No. 19 Xinjiekou Wai Street, Haidian District, Beijing 100875, China
- Beijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, No. 19 Xinjiekou Wai Street, Haidian District, Beijing 100875, China
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27
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Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy. Neuroimage 2016; 143:91-105. [PMID: 27591921 PMCID: PMC5139986 DOI: 10.1016/j.neuroimage.2016.08.058] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 06/29/2016] [Accepted: 08/29/2016] [Indexed: 12/14/2022] Open
Abstract
Haemodynamics-based neuroimaging is widely used to study brain function. Regional blood flow changes characteristic of neurovascular coupling provide an important marker of neuronal activation. However, changes in systemic physiological parameters such as blood pressure and concentration of CO2 can also affect regional blood flow and may confound haemodynamics-based neuroimaging. Measurements with functional near-infrared spectroscopy (fNIRS) may additionally be confounded by blood flow and oxygenation changes in extracerebral tissue layers. Here we investigate these confounds using an extended version of an existing computational model of cerebral physiology, ‘BrainSignals’. Our results show that confounding from systemic physiological factors is able to produce misleading haemodynamic responses in both positive and negative directions. By applying the model to data from previous fNIRS studies, we demonstrate that such potentially deceptive responses can indeed occur in at least some experimental scenarios. It is therefore important to record the major potential confounders in the course of fNIRS experiments. Our model may then allow the observed behaviour to be attributed among the potential causes and hence reduce identification errors. Confounding of fNIRS haemoglobin signals is simulated using a computational model. Model is extended to simulate scalp haemodynamics. Changes in blood pressure and CO2 can mimic and mask functional activation. Experimental recording of systemic factors is recommended to aid interpretation.
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28
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Yücel MA, Selb J, Aasted CM, Lin PY, Borsook D, Becerra L, Boas DA. Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2016; 7:3078-88. [PMID: 27570699 PMCID: PMC4986815 DOI: 10.1364/boe.7.003078] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 06/30/2016] [Accepted: 07/05/2016] [Indexed: 05/07/2023]
Abstract
Analysis of cerebral hemodynamics reveals a wide spectrum of oscillations ranging from 0.0095 to 2 Hz. While most of these oscillations can be filtered out during analysis of functional near-infrared spectroscopy (fNIRS) signals when estimating stimulus evoked hemodynamic responses, oscillations around 0.1 Hz are an exception. This is due to the fact that they share a common spectral range with typical stimulus evoked hemodynamic responses from the brain. Here we investigate the effect of hemodynamic oscillations around 0.1 Hz on the estimation of hemodynamic response functions from fNIRS data. Our results show that for an expected response of ~1 µM in oxygenated hemoglobin concentration (HbO), Mayer wave oscillations with an amplitude > ~1 µM at 0.1 Hz reduce the accuracy of the estimated response as quantified by a 3 fold increase in the mean squared error and decrease in correlation (R(2) below 0.78) when compared to the true HRF. These results indicate that the amplitude of oscillations at 0.1 Hz can serve as an objective metric of the expected HRF estimation accuracy. In addition, we investigated the effect of short separation regression on the recovered HRF, and found that this improves the recovered HRF when large amplitude 0.1 Hz oscillations are present in fNIRS data. We suspect that the development of other filtering strategies may provide even further improvement.
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Affiliation(s)
- Meryem A. Yücel
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - Juliette Selb
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - Christopher M. Aasted
- Center for Pain and the Brain, Departments of Anaesthesia and Radiology, Boston Children’s Hospital, Boston, MA, USA
| | - Pei-Yi Lin
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - David Borsook
- Center for Pain and the Brain, Departments of Anaesthesia and Radiology, Boston Children’s Hospital, Boston, MA, USA
| | - Lino Becerra
- Center for Pain and the Brain, Departments of Anaesthesia and Radiology, Boston Children’s Hospital, Boston, MA, USA
| | - David A. Boas
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
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29
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Harrivel AR, Weissman DH, Noll DC, Huppert T, Peltier SJ. Dynamic filtering improves attentional state prediction with fNIRS. BIOMEDICAL OPTICS EXPRESS 2016; 7:979-1002. [PMID: 27231602 PMCID: PMC4866469 DOI: 10.1364/boe.7.000979] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Revised: 02/12/2016] [Accepted: 02/14/2016] [Indexed: 05/23/2023]
Abstract
Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%).
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Affiliation(s)
- Angela R. Harrivel
- Crew Systems & Aviation Operations Branch, NASA Langley Research Center, Hampton, VA, 23681, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Daniel H. Weissman
- Department of Psychology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Douglas C. Noll
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Theodore Huppert
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Scott J. Peltier
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, 48109, USA
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30
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Huppert TJ. Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. NEUROPHOTONICS 2016; 3:010401. [PMID: 26989756 PMCID: PMC4773699 DOI: 10.1117/1.nph.3.1.010401] [Citation(s) in RCA: 126] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 01/22/2016] [Indexed: 05/18/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementation in other fields, particularly for functional magnetic resonance imaging, the specific application of these methods in fNIRS research differs in several key ways related to the sources of noise and artifacts unique to fNIRS. In this brief communication, we discuss the application of linear regression models in fNIRS and the modifications needed to generalize these models in order to deal with structured (colored) noise due to systemic physiology and noise heteroscedasticity due to motion artifacts. The objective of this work is to present an overview of these noise properties in the context of the linear model as it applies to fNIRS data. This work is aimed at explaining these mathematical issues to the general fNIRS experimental researcher but is not intended to be a complete mathematical treatment of these concepts.
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Affiliation(s)
- Theodore J. Huppert
- University of Pittsburgh, Center for the Neural Basis of Cognition, Clinical Science Translational Institute, Departments of Radiology and Bioengineering, Pittsburgh, Pennsylvania 15260, United States
- Address all correspondence to: Theodore J. Huppert, E-mail:
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31
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Baker WB, Parthasarathy AB, Ko TS, Busch DR, Abramson K, Tzeng SY, Mesquita RC, Durduran T, Greenberg JH, Kung DK, Yodh AG. Pressure modulation algorithm to separate cerebral hemodynamic signals from extracerebral artifacts. NEUROPHOTONICS 2015; 2:035004. [PMID: 26301255 PMCID: PMC4524732 DOI: 10.1117/1.nph.2.3.035004] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 07/01/2015] [Indexed: 05/18/2023]
Abstract
We introduce and validate a pressure measurement paradigm that reduces extracerebral contamination from superficial tissues in optical monitoring of cerebral blood flow with diffuse correlation spectroscopy (DCS). The scheme determines subject-specific contributions of extracerebral and cerebral tissues to the DCS signal by utilizing probe pressure modulation to induce variations in extracerebral blood flow. For analysis, the head is modeled as a two-layer medium and is probed with long and short source-detector separations. Then a combination of pressure modulation and a modified Beer-Lambert law for flow enables experimenters to linearly relate differential DCS signals to cerebral and extracerebral blood flow variation without a priori anatomical information. We demonstrate the algorithm's ability to isolate cerebral blood flow during a finger-tapping task and during graded scalp ischemia in healthy adults. Finally, we adapt the pressure modulation algorithm to ameliorate extracerebral contamination in monitoring of cerebral blood oxygenation and blood volume by near-infrared spectroscopy.
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Affiliation(s)
- Wesley B. Baker
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
- Address all correspondence to: Wesley B. Baker, E-mail:
| | - Ashwin B. Parthasarathy
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
| | - Tiffany S. Ko
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
| | - David R. Busch
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
- Children’s Hospital of Philadelphia, Division of Neurology, 3401 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
| | - Kenneth Abramson
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
| | - Shih-Yu Tzeng
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
- National Cheng Kung University, Department of Photonics, No. 1, University Road, Tainan City 701, Taiwan
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, 777 R. Sergio Buarque de Holanda, Campinas 13083-859, Brazil
| | - Turgut Durduran
- ICFO-Institut de Ciències Fotòniques, Mediterranean Technology Park, Av. Carl Friedrich Gauss 3, Castelldefels (Barcelona) 08860, Spain
| | - Joel H. Greenberg
- University of Pennsylvania, Department of Neurology, 3450 Hamilton Walk, Philadelphia, Pennsylvania 19104, United States
| | - David K. Kung
- Hospital of the University of Pennsylvania, Department of Neurosurgery, 3400 Spruce Street, Philadelphia, Pennsylvania 19104, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, 3231 Walnut Street, Philadelphia, Pennsylvania 19104, United States
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Shah A, Seghouane AK. An integrated framework for joint HRF and drift estimation and HbO/HbR signal improvement in fNIRS data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2086-97. [PMID: 24956281 DOI: 10.1109/tmi.2014.2331363] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Nonparametric hemodynamic response function (HRF) estimation in functional near-infrared spectroscopy (fNIRS) data plays an important role when investigating the temporal dynamics of a brain region response during activations. Assuming the drift arising from both physical and physiological effects in fNIRS data is Lipschitz continuous; a novel algorithm for joint HRF and drift estimation is derived in this paper. The proposed algorithm estimates the HRF by applying a first-order differencing to the fNIRS time series samples in order to remove the drift effect. An estimate of the drift is then obtained using a wavelet thresholding technique applied to the residuals generated by removing the estimated induced activation response from the fNIRS time-series. It is shown that the proposed HRF estimator is √N consistent whereas the estimator of the drift is asymptotically optimal. The de-drifted fNIRS oxygenated (HbO) and deoxygenated (HbR) hemoglobin responses are then obtained by removing the corresponding estimated drifts from the fNIRS time-series. Its performance is assessed using both simulated and real fNIRS data sets. The application results reveal that the proposed joint HRF and drift estimation method is efficient both computationally and in terms of accuracy. In comparison to traditional model based methods used for HRF estimation, the proposed novel method avoids the selection of a model to remove the drift component. As a result, the proposed method finds an optimal estimate of the fNIRS drift and offers a model-free approach to de-drift the HbO/HbR responses.
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Giacometti P, Diamond SG. Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout. NEUROPHOTONICS 2014; 1:025001. [PMID: 25558462 PMCID: PMC4280681 DOI: 10.1117/1.nph.1.2.025001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R = 0.46 ± 0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R = 0.43 ± 0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization.
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Affiliation(s)
- Paolo Giacometti
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, New Hampshire 03755, United States
- Address all correspondence to: Paolo Giacometti, E-mail:
| | - Solomon G. Diamond
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, New Hampshire 03755, United States
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Analysis of task-evoked systemic interference in fNIRS measurements: Insights from fMRI. Neuroimage 2014; 87:490-504. [DOI: 10.1016/j.neuroimage.2013.10.024] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 10/09/2013] [Accepted: 10/12/2013] [Indexed: 11/21/2022] Open
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Gagnon L, Yücel MA, Boas DA, Cooper RJ. Further improvement in reducing superficial contamination in NIRS using double short separation measurements. Neuroimage 2014. [PMID: 23403181 DOI: 10.1016/j.neuroimage.2013.01.073.further] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023] Open
Abstract
Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.
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Affiliation(s)
- Louis Gagnon
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
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Scholkmann F, Kleiser S, Metz AJ, Zimmermann R, Mata Pavia J, Wolf U, Wolf M. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage 2014; 85 Pt 1:6-27. [PMID: 23684868 DOI: 10.1016/j.neuroimage.2013.05.004] [Citation(s) in RCA: 1000] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 04/12/2013] [Accepted: 05/03/2013] [Indexed: 01/09/2023] Open
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Further improvement in reducing superficial contamination in NIRS using double short separation measurements. Neuroimage 2013; 85 Pt 1:127-35. [PMID: 23403181 DOI: 10.1016/j.neuroimage.2013.01.073] [Citation(s) in RCA: 129] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Revised: 01/23/2013] [Accepted: 01/29/2013] [Indexed: 11/24/2022] Open
Abstract
Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.
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Barker JW, Aarabi A, Huppert TJ. Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS. BIOMEDICAL OPTICS EXPRESS 2013; 4:1366-79. [PMID: 24009999 PMCID: PMC3756568 DOI: 10.1364/boe.4.001366] [Citation(s) in RCA: 203] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Revised: 06/07/2013] [Accepted: 06/08/2013] [Indexed: 05/02/2023]
Abstract
Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3-5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5-9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present.
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Affiliation(s)
- Jeffrey W. Barker
- Department of Radiology, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260,
USA
- Department of Bioengineering, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260,
USA
| | - Ardalan Aarabi
- Department of Radiology, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260,
USA
- GRAMFC, Faculty of Medicine, University of Picardie-Jules Verne, 3 rue des Louvels, Amiens Cedex, 80036,
France
| | - Theodore J. Huppert
- Department of Radiology, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260,
USA
- Department of Bioengineering, University of Pittsburgh, 4200 Fifth Avenue, Pittburgh, PA 15260,
USA
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Chuang CC, Lee YT, Chen CM, Hsieh YS, Liu TC, Sun CW. Patient-oriented simulation based on Monte Carlo algorithm by using MRI data. Biomed Eng Online 2012; 11:21. [PMID: 22510474 PMCID: PMC3355000 DOI: 10.1186/1475-925x-11-21] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2011] [Accepted: 04/17/2012] [Indexed: 11/10/2022] Open
Abstract
Background Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation. Methods In this study, an individualized brain is modeled based on in vivo MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system. Results Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement. Conclusions In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.
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Affiliation(s)
- Ching-Cheng Chuang
- Institute of Biomedical Engineering and National Taiwan University Molecular Imaging Center, National Taiwan University, Taipei, Taiwan, Republic of China
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40
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Pogue BW, Davis SC, Leblond F, Mastanduno MA, Dehghani H, Paulsen KD. Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2011; 369:4531-57. [PMID: 22006905 PMCID: PMC3263784 DOI: 10.1098/rsta.2011.0228] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.
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Affiliation(s)
- Brian W Pogue
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
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41
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Cooper RJ, Gagnon L, Goldenholz DM, Boas DA, Greve DN. The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data. Neuroimage 2011; 59:3128-38. [PMID: 22119653 DOI: 10.1016/j.neuroimage.2011.11.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2011] [Revised: 10/06/2011] [Accepted: 11/09/2011] [Indexed: 10/15/2022] Open
Abstract
Near-infrared spectroscopy (NIRS) signals have been shown to correlate with resting-state BOLD-fMRI data across the whole brain volume, particularly at frequencies below 0.1Hz. While the physiological origins of this correlation remain unclear, its existence may have a practical application in minimizing the background physiological noise present in BOLD-fMRI recordings. We performed simultaneous, resting-state fMRI and 28-channel NIRS in seven adult subjects in order to assess the utility of NIRS signals in the regression of physiological noise from fMRI data. We calculated the variance of the residual error in a general linear model of the baseline fMRI signal, and the reduction of this variance achieved by including NIRS signals in the model. In addition, we introduced a sequence of simulated hemodynamic response functions (HRFs) into the resting-state fMRI data of each subject in order to quantify the effectiveness of NIRS signals in optimizing the recovery of that HRF. For comparison, these calculations were also performed using a pulse and respiration RETROICOR model. Our results show that the use of 10 or more NIRS channels can reduce variance in the residual error by as much as 36% on average across the whole cortex. However the same number of low-pass filtered white noise regressors is shown to produce a reduction of 19%. The RETROICOR model obtained a variance reduction of 6.4%. Our HRF simulation showed that the mean-squared error (MSE) between the recovered and true HRFs is reduced by 21% on average when 10 NIRS channels are applied and by introducing an optimized time lag between the NIRS and fMRI time series, a single NIRS channel can provide an average MSE reduction of 14%. The RETROICOR model did not provide a significant change in MSE. By each of the metrics calculated, NIRS recording is shown to be of significant benefit to the regression of low-frequency physiological noise from fMRI data.
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Affiliation(s)
- R J Cooper
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
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42
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Wang CY, Chuang ML, Liang SJ, Tsai JC, Chuang CC, Hsieh YS, Lu CW, Lee PL, Sun CW. Diffuse optical multipatch technique for tissue oxygenation monitoring: clinical study in intensive care unit. IEEE Trans Biomed Eng 2011; 59:87-94. [PMID: 21536517 DOI: 10.1109/tbme.2011.2147315] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diffuse optical multipatch technique is used to assess spatial variations in absorption and scattering in biological tissue, by monitoring changes in the concentration of oxyhemoglobin and deoxyhemoglobin. In our preliminary study, the temporal tracings of tissue oxygenation are measured using diffuse optical multipatch measurement and a venous occlusion test, employing normal subjects and ICU patients suffering from sepsis and heart failure. In experiments, obvious differences in tissue oxygenation signals were observed among all three groups. This paper discusses the physiological relevance of tissue oxygenation with respect to disease.
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Affiliation(s)
- Chun-Yang Wang
- Department of Photonics, National Chiao Tung University, Hsinchu 30010, Taiwan
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Gagnon L, Perdue K, Greve DN, Goldenholz D, Kaskhedikar G, Boas DA. Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling. Neuroimage 2011; 56:1362-71. [PMID: 21385616 DOI: 10.1016/j.neuroimage.2011.03.001] [Citation(s) in RCA: 183] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2010] [Revised: 02/19/2011] [Accepted: 03/01/2011] [Indexed: 11/28/2022] Open
Abstract
Diffuse optical imaging (DOI) allows the recovery of the hemodynamic response associated with evoked brain activity. The signal is contaminated with systemic physiological interference which occurs in the superficial layers of the head as well as in the brain tissue. The back-reflection geometry of the measurement makes the DOI signal strongly contaminated by systemic interference occurring in the superficial layers. A recent development has been the use of signals from small source-detector separation (1cm) optodes as regressors. Since those additional measurements are mainly sensitive to superficial layers in adult humans, they help in removing the systemic interference present in longer separation measurements (3 cm). Encouraged by those findings, we developed a dynamic estimation procedure to remove global interference using small optode separations and to estimate simultaneously the hemodynamic response. The algorithm was tested by recovering a simulated synthetic hemodynamic response added over baseline DOI data acquired from 6 human subjects at rest. The performance of the algorithm was quantified by the Pearson R(2) coefficient and the mean square error (MSE) between the recovered and the simulated hemodynamic responses. Our dynamic estimator was also compared with a static estimator and the traditional adaptive filtering method. We observed a significant improvement (two-tailed paired t-test, p<0.05) in both HbO and HbR recovery using our Kalman filter dynamic estimator compared to the traditional adaptive filter, the static estimator and the standard GLM technique.
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Affiliation(s)
- Louis Gagnon
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
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Hu XS, Hong KS, Ge SS, Jeong MY. Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy. Biomed Eng Online 2010; 9:82. [PMID: 21138595 PMCID: PMC3020171 DOI: 10.1186/1475-925x-9-82] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2010] [Accepted: 12/08/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. To date, for functional brain mapping applications, there is no standard on-line method for analysing NIRS data. METHODS In this paper, a novel on-line NIRS data analysis framework taking advantages of both the general linear model (GLM) and the Kalman estimator is devised. The Kalman estimator is used to update the GLM coefficients recursively, and one critical coefficient regarding brain activities is then passed to a t-statistical test. The t-statistical test result is used to update a topographic brain activation map. Meanwhile, a set of high-pass filters is plugged into the GLM to prevent very low-frequency noises, and an autoregressive (AR) model is used to prevent the temporal correlation caused by physiological noises in NIRS time series. A set of data recorded in finger tapping experiments is studied using the proposed framework. RESULTS The obtained results suggest that the method can effectively track the task related brain activation areas, and prevent the noise distortion in the estimation while the experiment is running. Thereby, the potential of the proposed method for real-time NIRS-based brain imaging was demonstrated. CONCLUSIONS This paper presents a novel on-line approach for analysing NIRS data for functional brain mapping applications. This approach demonstrates the potential of a real-time-updating topographic brain activation map.
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Affiliation(s)
- Xiao-Su Hu
- Department of Cogno-Mechatronics Engineering, Pusan National University; 30 Jangjeon-dong, Geumjeong-gu, Busan609-735, Korea
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Scarpa F, Cutini S, Scatturin P, Dell'Acqua R, Sparacino G. Bayesian filtering of human brain hemodynamic activity elicited by visual short-term maintenance recorded through functional near-infrared spectroscopy (fNIRS). OPTICS EXPRESS 2010; 18:26550-26568. [PMID: 21165006 DOI: 10.1364/oe.18.026550] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that measures changes in oxy-hemoglobin (ΔHbO) and deoxy-hemoglobin (ΔHbR) concentration associated with brain activity. The signal acquired with fNIRS is naturally affected by disturbances engendering from ongoing physiological activity (e.g., cardiac, respiratory, Mayer wave) and random measurement noise. Despite its several drawbacks, the so-called conventional averaging (CA) is still widely used to estimate the hemodynamic response function (HRF) from noisy signal. One such drawback is related to the number of trials necessary to derive stable HRF functions adopting the CA approach, which must be substantial (N >> 50). In this work, a pre-processing procedure to remove artifacts followed by the application of a non-parametric Bayesian approach is proposed that capitalizes on a priori available knowledge about HRF and noise. Results with the proposed Bayesian approach were compared with CA and with a straightforward band-pass filtering approach. On simulated data, a five times lower estimation error on HRF was obtained with respect to that obtained by CA, and 2.5 times lower than that obtained by band pass filtering. On real data, the improvement achieved by the present method was attested by an increase in the contrast to noise ratio (CNR) and by a reduced variability in single trial estimation. An application of the present Bayesian approach is illustrated that was optimized to monitor changes in hemodynamic activity reflecting variations in visual short-term memory load in humans, which are notoriously hard to detect using functional magnetic resonance imaging (fMRI). In particular, statistical analyses of HRFs recorded during a memory task established with high reliability the crucial role of the intraparietal sulcus and the intra-occipital sulcus in posterior areas of the human brain in visual short-term memory maintenance.
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Affiliation(s)
- F Scarpa
- Department of Developmental Psychology, University of Padova, Via Venezia 8, Padova 35131, Italy.
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46
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Mesquita RC, Franceschini MA, Boas DA. Resting state functional connectivity of the whole head with near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2010; 1:324-336. [PMID: 21258470 PMCID: PMC3005169 DOI: 10.1364/boe.1.000324] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2010] [Revised: 06/24/2010] [Accepted: 07/27/2010] [Indexed: 05/18/2023]
Abstract
Resting state connectivity aims to identify spontaneous cerebral hemodynamic fluctuations that reflect neuronal activity at rest. In this study, we investigated the spatial-temporal correlation of hemoglobin concentration signals over the whole head during the resting state. By choosing a source-detector pair as a seed, we calculated the correlation value between its time course and the time course of all other source-detector combinations, and projected them onto a topographic map. In all subjects, we found robust spatial interactions in agreement with previous fMRI and NIRS findings. Strong correlations between the two opposite hemispheres were seen for both sensorimotor and visual cortices. Correlations in the prefrontal cortex were more heterogeneous and dependent on the hemodynamic contrast. HbT provided robust, well defined maps, suggesting that this contrast may be used to better localize functional connectivity. The effects of global systemic physiology were also investigated, particularly low frequency blood pressure oscillations which give rise to broad regions of high correlation and mislead interpretation of the results. These results confirm the feasibility of using functional connectivity with optical methods during the resting state, and validate its use to investigate cortical interactions across the whole head.
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Affiliation(s)
- Rickson C. Mesquita
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Charlestown, MA 02129, USA
- Department of Physics & Astronomy, University of Pennsylvania, 209 South 33rd St., Philadelphia, PA 19104, USA
| | - Maria A. Franceschini
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Charlestown, MA 02129, USA
| | - David A. Boas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Charlestown, MA 02129, USA
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Durduran T, Choe R, Baker WB, Yodh AG. Diffuse Optics for Tissue Monitoring and Tomography. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2010; 73:076701. [PMID: 26120204 PMCID: PMC4482362 DOI: 10.1088/0034-4885/73/7/076701] [Citation(s) in RCA: 561] [Impact Index Per Article: 40.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This review describes the diffusion model for light transport in tissues and the medical applications of diffuse light. Diffuse optics is particularly useful for measurement of tissue hemodynamics, wherein quantitative assessment of oxy- and deoxy-hemoglobin concentrations and blood flow are desired. The theoretical basis for near-infrared or diffuse optical spectroscopy (NIRS or DOS, respectively) is developed, and the basic elements of diffuse optical tomography (DOT) are outlined. We also discuss diffuse correlation spectroscopy (DCS), a technique whereby temporal correlation functions of diffusing light are transported through tissue and are used to measure blood flow. Essential instrumentation is described, and representative brain and breast functional imaging and monitoring results illustrate the workings of these new tissue diagnostics.
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Affiliation(s)
- T Durduran
- ICFO- Institut de Ciències Fotòniques, Mediterranean Technology Park, 08860 Castelldefels (Barcelona), Spain
| | - R Choe
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - W B Baker
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - A G Yodh
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
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Orihuela-Espina F, Leff DR, James DRC, Darzi AW, Yang GZ. Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation. Phys Med Biol 2010; 55:3701-24. [PMID: 20530852 DOI: 10.1088/0031-9155/55/13/009] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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49
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Abdelnour F, Schmidt B, Huppert TJ. Topographic localization of brain activation in diffuse optical imaging using spherical wavelets. Phys Med Biol 2009; 54:6383-413. [PMID: 19809125 PMCID: PMC2806654 DOI: 10.1088/0031-9155/54/20/023] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Diffuse optical imaging is a non-invasive technique that uses near-infrared light to measure changes in brain activity through an array of sensors placed on the surface of the head. Compared to functional MRI, optical imaging has the advantage of being portable while offering the ability to record functional changes in both oxy- and deoxy-hemoglobin within the brain at a high temporal resolution. However, the reconstruction of accurate spatial images of brain activity from optical measurements represents an ill-posed and underdetermined problem that requires regularization. These reconstructions benefit from incorporating prior information about the underlying spatial structure and function of the brain. In this work, we describe a novel image reconstruction approach which uses surface-based wavelets derived from structural MRI to incorporate high-resolution anatomical and structural prior information about the brain. This surface-based approach is used to approximate brain activation patterns through the reconstruction and presentation of topographical (two-dimensional) maps of brain activation directly onto the folded surface of the cortex. The set of wavelet coefficients is directly estimated by a truncated singular-value decomposition based pseudo-inversion of the wavelet projection of the optical forward model. We use a reconstruction metric based on Shannon entropy which quantifies the sparse loading of the wavelet coefficients and is used to determine the optimal truncation and regularization of this inverse model. In this work, examples of the performance of this model are illustrated for several cases of numerical simulation and experimental data with comparison to functional magnetic resonance imaging.
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Affiliation(s)
- F Abdelnour
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
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Virtanen J, Noponen T, Meriläinen P. Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals. JOURNAL OF BIOMEDICAL OPTICS 2009; 14:054032. [PMID: 19895134 DOI: 10.1117/1.3253323] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Near-infrared spectroscopy (NIRS) is a method for noninvasive estimation of cerebral hemodynamic changes. Principal component analysis (PCA) and independent component analysis (ICA) can be used for decomposing a set of signals to underlying components. Our objective is to determine whether PCA or ICA is more efficient in identifying and removing scalp blood flow interference from multichannel NIRS signals. Concentration changes of oxygenated (HbO(2)) and deoxygenated (HbR) hemoglobin are measured on the forehead with multichannel NIRS during hyper- and hypocapnia. PCA and ICA are used separately to identify and remove signal contribution from extracerebral tissue, and the resulting estimates of cerebral responses are compared to the expected cerebral responses. Both methods were able to reduce extracerebral contribution to the signals, but PCA typically performs equal to or better than ICA. The improvement in 3-cm signal quality achieved with both methods is comparable to increasing the source-detector separation from 3 to 5 cm. Especially PCA appears to be well suited for use in NIRS applications where the cerebral activation is diffuse, such as monitoring of global cerebral oxygenation and hemodynamics. Performance differences between PCA and ICA could be attributed primarily to different criteria for identifying the surface effect.
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Affiliation(s)
- Jaakko Virtanen
- Helsinki University of Technology, Department of Biomedical Engineering and Computational Science, P.O. Box 3310, FI-02015 TKK, Helsinki, Finland.
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