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Curzel F, Tillmann B, Ferreri L. Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives. Brain Cogn 2024; 180:106200. [PMID: 38908228 DOI: 10.1016/j.bandc.2024.106200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 06/10/2024] [Accepted: 06/16/2024] [Indexed: 06/24/2024]
Abstract
Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.
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Affiliation(s)
- Federico Curzel
- Laboratoire d'Étude des Mécanismes Cognitifs (EMC), Université Lumière Lyon 2, Bron, Auvergne-Rhône-Alpes, 69500, France; Lyon Neuroscience Research Center (CRNL), INSERM, U1028, CNRS, UMR 5292, Université Claude Bernard Lyon1, Université de Lyon, Bron, Auvergne-Rhône-Alpes, 69500, France.
| | - Barbara Tillmann
- Lyon Neuroscience Research Center (CRNL), INSERM, U1028, CNRS, UMR 5292, Université Claude Bernard Lyon1, Université de Lyon, Bron, Auvergne-Rhône-Alpes, 69500, France; LEAD CNRS UMR5022, Université de Bourgogne-Franche Comté, Dijon, Bourgogne-Franche Comté 21000, France.
| | - Laura Ferreri
- Laboratoire d'Étude des Mécanismes Cognitifs (EMC), Université Lumière Lyon 2, Bron, Auvergne-Rhône-Alpes, 69500, France; Department of Brain and Behavioural Sciences, Università di Pavia, Pavia, Lombardia 27100, Italy.
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McCann A, Xu E, Yen FY, Joseph N, Fang Q. Creating anatomically-derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.30.610386. [PMID: 39257741 PMCID: PMC11383710 DOI: 10.1101/2024.08.30.610386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Significance Consistent and accurate probe placement is a crucial step towards enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. While the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures. Aim We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, 3-D printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap. Approach We utilize our advanced 3-D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3-D printable head cap model. The 3-D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain". Results Using the intuitive user interface, we create various head cap models using brain atlases, and share those with the community. The resulting mesh-based head cap designs are readily 3-D printable using off-the-shelf printers and filaments while accurately preserving the head topology and landmarks. Conclusions The methods developed in this work result in a widely accessible tool for community members to design, customize and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy, but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.
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Affiliation(s)
- Ashlyn McCann
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Edward Xu
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Fan-Yu Yen
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Noah Joseph
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Northeastern University, Department of EECS, 360 Huntington Avenue, Boston, USA, 02115
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Xu E, Vanegas M, Mireles M, Dementyev A, McCann A, Yücel M, Carp SA, Fang Q. Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings. NEUROPHOTONICS 2024; 11:035002. [PMID: 38975286 PMCID: PMC11224775 DOI: 10.1117/1.nph.11.3.035002] [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: 02/12/2024] [Revised: 05/31/2024] [Accepted: 06/07/2024] [Indexed: 07/09/2024]
Abstract
Significance Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. Aim The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. Approach The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Results Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3 pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18 g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.
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Affiliation(s)
- Edward Xu
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Morris Vanegas
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Miguel Mireles
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Artem Dementyev
- Massachusetts Institute of Technology, Media Lab, Cambridge, Massachusetts, United States
| | - Ashlyn McCann
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Meryem Yücel
- Boston University, Neurophotonics Center, Boston, Massachusetts, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
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von Lühmann A, Kura S, Joseph O’Brien W, Zimmermann BB, Duwadi S, Rogers D, Anderson JE, Farzam P, Snow C, Chen A, Yücel MA, Perkins N, Boas DA. ninjaCap: a fully customizable and 3D printable headgear for functional near-infrared spectroscopy and electroencephalography brain imaging. NEUROPHOTONICS 2024; 11:036601. [PMID: 39193445 PMCID: PMC11348010 DOI: 10.1117/1.nph.11.3.036601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/12/2024] [Accepted: 07/24/2024] [Indexed: 08/29/2024]
Abstract
Accurate sensor placement is vital for non-invasive brain imaging, particularly for functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts such as those in electroencephalography (EEG). Custom, manually prepared probe layouts on textile caps are often imprecise and labor intensive. We introduce a method for creating personalized, 3D-printed headgear, enabling the accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts while reducing costs and manual labor. Our approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. Our ninjaCap method offers 2.7 ± 1.8 mm probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. The ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.
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Affiliation(s)
- Alexander von Lühmann
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Technical University Berlin, Intelligent Biomedical Sensing Lab, Berlin, Germany
| | - Sreekanth Kura
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Walker Joseph O’Brien
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Bernhard B. Zimmermann
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Sudan Duwadi
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - De’Ja Rogers
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Jessica E. Anderson
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Parya Farzam
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Cameron Snow
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Anderson Chen
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Meryem A. Yücel
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Nathan Perkins
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - David A. Boas
- Boston University, Neurophotonics Center, Department of Biomedical Engineering, Boston, Massachusetts, United States
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von Lühmann A, Kura S, O'Brien WJ, Zimmermann BB, Duwadi S, Rogers D, Anderson JE, Farzam P, Snow C, Chen A, Yücel MA, Perkins N, Boas DA. ninjaCap: A fully customizable and 3D printable headgear for fNIRS and EEG brain imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594159. [PMID: 38798389 PMCID: PMC11118375 DOI: 10.1101/2024.05.14.594159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Significance Accurate sensor placement is vital for non-invasive brain imaging, particularly for functional near infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts like EEG. Custom, manually prepared probe layouts on textile caps are often imprecise and labor-intensive. Aim We introduce a method for creating personalized, 3D-printed headgear, enabling accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts, reducing costs and manual labor. Approach Our approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. Results Our ninjaCap method offers 2.2±1.5 mm probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. Conclusions The ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.
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Srinivasan S, Acharya D, Butters E, Collins-Jones L, Mancini F, Bale G. Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography. FRONTIERS IN NEUROERGONOMICS 2024; 5:1283290. [PMID: 38444841 PMCID: PMC10910052 DOI: 10.3389/fnrgo.2024.1283290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical activation using fNIRS data is highly dependent on the ability to correctly localize the positions of light sources and photodetectors on the scalp surface. Variations in head size and shape across participants greatly impact the precise locations of these optodes and consequently, the regions of the cortical surface being reached. Such variations can therefore influence the conclusions drawn in NIRS studies that attempt to explore specific cortical regions. In order to preserve the spatial identity of each NIRS channel, subject-specific differences in NIRS array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated the inter-subject variability of the same HD-DOT array applied to ten participants recorded in the resting state. We have also compared three-dimensional image reconstruction results obtained using subject-specific positioning information to those obtained using generic optode locations. To mitigate the error introduced by using generic information for all participants, photogrammetry was used to identify specific optode locations per-participant. The present work demonstrates the large variation between subjects in terms of which cortical parcels are sampled by equivalent channels in the HD-DOT array. In particular, motor cortex recordings suffered from the largest optode localization errors, with a median localization error of 27.4 mm between generic and subject-specific optodes, leading to large differences in parcel sensitivity. These results illustrate the importance of collecting subject-specific optode locations for all wearable NIRS experiments, in order to perform accurate group-level analysis using cortical parcellation.
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Affiliation(s)
- Sruthi Srinivasan
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Deepshikha Acharya
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Emilia Butters
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Liam Collins-Jones
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Flavia Mancini
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Gemma Bale
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
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Leadley G, Austin T, Bale G. Review of measurements and imaging of cytochrome-c-oxidase in humans using near-infrared spectroscopy: an update. BIOMEDICAL OPTICS EXPRESS 2024; 15:162-184. [PMID: 38223181 PMCID: PMC10783912 DOI: 10.1364/boe.501915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/21/2023] [Indexed: 01/16/2024]
Abstract
This review examines advancements in the measurement and imaging of oxidized cytochrome-c-oxidase (oxCCO) using near-infrared spectroscopy (NIRS) in humans since 2016. A total of 34 published papers were identified, with a focus on both adult and neonate populations. The NIRS-derived oxCCO signal has been demonstrated to correlate with physiological parameters and hemodynamics. New instrumentation, such as systems that allow the imaging of changes of oxCCO with diffuse optical tomography or combine the oxCCO measurement with diffuse correlation spectroscopy measures of blood flow, have advanced the field in the past decade. However, variability in its response across different populations and paradigms and lack of standardization limit its potential as a reliable and valuable indicator of brain health. Future studies should address these issues to fulfill the vision of oxCCO as a clinical biomarker.
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Affiliation(s)
- Georgina Leadley
- Department of Paediatrics, University of Cambridge, UK
- Department of Engineering, University of Cambridge, UK
- Department of Medical Physics and Biomedical Engineering, UCL, UK
| | - Topun Austin
- Department of Paediatrics, University of Cambridge, UK
| | - Gemma Bale
- Department of Engineering, University of Cambridge, UK
- Department of Physics, University of Cambridge, UK
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Quispe-Enriquez OC, Valero-Lanzuela JJ, Lerma JL. Craniofacial 3D Morphometric Analysis with Smartphone-Based Photogrammetry. SENSORS (BASEL, SWITZERLAND) 2023; 24:230. [PMID: 38203091 PMCID: PMC10781299 DOI: 10.3390/s24010230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024]
Abstract
Obtaining 3D craniofacial morphometric data is essential in a variety of medical and educational disciplines. In this study, we explore smartphone-based photogrammetry with photos and video recordings as an effective tool to create accurate and accessible metrics from head 3D models. The research involves the acquisition of craniofacial 3D models on both volunteers and head mannequins using a Samsung Galaxy S22 smartphone. For the photogrammetric processing, Agisoft Metashape v 1.7 and PhotoMeDAS software v 1.7 were used. The Academia 50 white-light scanner was used as reference data (ground truth). A comparison of the obtained 3D meshes was conducted, yielding the following results: 0.22 ± 1.29 mm for photogrammetry with camera photos, 0.47 ± 1.43 mm for videogrammetry with video frames, and 0.39 ± 1.02 mm for PhotoMeDAS. Similarly, anatomical points were measured and linear measurements extracted, yielding the following results: 0.75 mm for photogrammetry, 1 mm for videogrammetry, and 1.25 mm for PhotoMeDAS, despite large differences found in data acquisition and processing time among the four approaches. This study suggests the possibility of integrating photogrammetry either with photos or with video frames and the use of PhotoMeDAS to obtain overall craniofacial 3D models with significant applications in the medical fields of neurosurgery and maxillofacial surgery.
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Affiliation(s)
- Omar C. Quispe-Enriquez
- Photogrammetry and Laser Scanner Research Group (GIFLE), Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain; (J.J.V.-L.); (J.L.L.)
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Xia Y, Wang K, Billing A, Billing M, Cooper RJ, Zhao H. Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications. NEUROPHOTONICS 2023; 10:046601. [PMID: 37876984 PMCID: PMC10593123 DOI: 10.1117/1.nph.10.4.046601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
Significance To effectively apply functional near-infrared spectroscopy (fNIRS)/diffuse optical tomography (DOT) devices, a three-dimensional (3D) model of the position of each optode on a subject's scalp and the positions of that subject's cranial landmarks are critical. Obtaining this information accurately in infants, who rarely stop moving, is an ongoing challenge. Aim We propose a smartphone-based registration system that can potentially achieve a full-head 3D scan of a 6-month-old infant instantly. Approach The proposed system is remotely controlled by a custom-designed Bluetooth controller. The scanned images can either be manually or automatically aligned to generate a 3D head surface model. Results A full-head 3D scan of a 6-month-old infant can be achieved within 2 s via this system. In testing on a realistic but static infant head model, the average Euclidean error of optode position using this device was 1.8 mm. Conclusions This low-cost 3D registration system therefore has the potential to permit accurate and near-instant fNIRS/DOT spatial registration.
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Affiliation(s)
- Yunjia Xia
- University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Kui Wang
- University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Addison Billing
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- University of Cambridge, Prediction and Learning Laboratory, Department of Psychology, Cambridge, United Kingdom
| | - Matthew Billing
- London South Bank University, School of Engineering, London, United Kingdom
| | - Robert J. Cooper
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Hubin Zhao
- University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
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Poór VS. Phone cam array - An open-source, modular photogrammetry system made of Android phones. HARDWAREX 2023; 14:e00438. [PMID: 37288362 PMCID: PMC10242632 DOI: 10.1016/j.ohx.2023.e00438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/09/2023]
Abstract
Photogrammetry is a 3D reconstruction technique using photographs of the target from multiple angles. Taking pictures around a static object with a single camera can yield high-quality models, but if the subject moves between images, 3D reconstruction might fail. One way to mitigate this is to use multiple cameras. This project aimed to develop a tool for fast and precise wound documentation for clinical forensic medicine. This paper describes a simple, low-cost modular system, where smartphones of different manufacturers are used as networked cameras. Exposure is initiated at the same time in all the phones with a simple circuit emulating a headset button press. A proof-of-concept device was built, where four phones (Huawei nova 8i (2 pcs), Samsung Galaxy S7 Edge, Oukitel K4000 Pro) were attached to a curved, 3D-printed, handheld frame. The average delay of image capture was 636 ms between the quickest and the slowest phones. When compared to the single-camera approach, the use of different cameras did not reduce the quality of the 3D model. The phone cam array was less susceptible to movement artefacts caused by breathing. Wound assessment was possible based on the 3D models created with this device.
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Schroeder PA, Artemenko C, Kosie JE, Cockx H, Stute K, Pereira J, Klein F, Mehler DMA. Using preregistration as a tool for transparent fNIRS study design. NEUROPHOTONICS 2023; 10:023515. [PMID: 36908680 PMCID: PMC9993433 DOI: 10.1117/1.nph.10.2.023515] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 01/11/2023] [Indexed: 05/04/2023]
Abstract
Significance The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design and analytical decisions that researchers have to make. Several recent efforts have developed guidelines for preprocessing, analyzing, and reporting practices. For the planning stage of fNIRS studies, similar guidance is desirable. Study preregistration helps researchers to transparently document study protocols before conducting the study, including materials, methods, and analyses, and thus, others to verify, understand, and reproduce a study. Preregistration can thus serve as a useful tool for transparent, careful, and comprehensive fNIRS study design. Aim We aim to create a guide on the design and analysis steps involved in fNIRS studies and to provide a preregistration template specified for fNIRS studies. Approach The presented preregistration guide has a strong focus on fNIRS specific requirements, and the associated template provides examples based on continuous-wave (CW) fNIRS studies conducted in humans. These can, however, be extended to other types of fNIRS studies. Results On a step-by-step basis, we walk the fNIRS user through key methodological and analysis-related aspects central to a comprehensive fNIRS study design. These include items specific to the design of CW, task-based fNIRS studies, but also sections that are of general importance, including an in-depth elaboration on sample size planning. Conclusions Our guide introduces these open science tools to the fNIRS community, providing researchers with an overview of key design aspects and specification recommendations for comprehensive study planning. As such it can be used as a template to preregister fNIRS studies or merely as a tool for transparent fNIRS study design.
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Affiliation(s)
- Philipp A. Schroeder
- University of Tuebingen, Department of Psychology, Faculty of Science, Tuebingen, Germany
| | - Christina Artemenko
- University of Tuebingen, Department of Psychology, Faculty of Science, Tuebingen, Germany
| | - Jessica E. Kosie
- Princeton University, Social and Natural Sciences, Department of Psychology, Princeton, New Jersey, United States
| | - Helena Cockx
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Nijmegen, The Netherlands
| | - Katharina Stute
- Chemnitz University of Technology, Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz, Germany
| | - João Pereira
- University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research, Coimbra, Portugal
| | - Franziska Klein
- University of Oldenburg, Department of Psychology, Neurocognition and functional Neurorehabilitation Group, Oldenburg (Oldb), Germany
- RWTH Aachen University, Medical School, Department of Psychiatry, Psychotherapy and Psychosomatics, Aachen, Germany
| | - David M. A. Mehler
- RWTH Aachen University, Medical School, Department of Psychiatry, Psychotherapy and Psychosomatics, Aachen, Germany
- University of Münster, Institute for Translational Psychiatry, Medical School, Münster, Germany
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Jaiswal A, Nenonen J, Parkkonen L. On electromagnetic head digitization in MEG and EEG. Sci Rep 2023; 13:3801. [PMID: 36882438 PMCID: PMC9992397 DOI: 10.1038/s41598-023-30223-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
In MEG and EEG studies, the accuracy of the head digitization impacts the co-registration between functional and structural data. The co-registration is one of the major factors that affect the spatial accuracy in MEG/EEG source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also deform a template MRI. Such an individualized-template MRI can be used for conductivity modeling in MEG/EEG source imaging if the individual's structural MRI is unavailable. Electromagnetic tracking (EMT) systems (particularly Fastrak, Polhemus Inc., Colchester, VT, USA) have been the most common solution for digitization in MEG and EEG. However, they may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study-(i) evaluated the performance of the Fastrak EMT system under different conditions in MEG/EEG digitization, and (ii) explores the usability of two alternative EMT systems (Aurora, NDI, Waterloo, ON, Canada; Fastrak with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The performance of the two alternative systems was compared against the Fastrak system. The results showed that the Fastrak system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora system can be used for MEG/EEG digitization within a constrained range; however, some modifications would be required to make the system a practical and easy-to-use digitizer. Its real-time error estimation feature can potentially improve digitization accuracy.
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Affiliation(s)
- Amit Jaiswal
- MEGIN Oy, Espoo, Finland. .,Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
| | | | - Lauri Parkkonen
- MEGIN Oy, Espoo, Finland.,Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
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Fairclough SH, Stamp K, Dobbins C. Functional connectivity across dorsal and ventral attention networks in response to task difficulty and experimental pain. Neurosci Lett 2023; 793:136967. [PMID: 36379390 DOI: 10.1016/j.neulet.2022.136967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/15/2022]
Abstract
The dorsal and ventral attention networks (DAN & VAN) provide a framework for studying attentional modulation of pain. It has been argued that cognitive demand distracts attention from painful stimuli via top-down reinforcement of task goals (DAN), whereas pain exerts an interruptive effect on cognitive performance via bottom-up pathways (VAN). The current study explores this explanatory framework by manipulating pain and task demand in combination with functional near-infrared spectroscopy (fNIRS) and Granger Causal Connectivity Analyses (GCCA). Twenty-one participants played a racing game at low and high difficulty levels with or without experimental pain (administered via a cold pressor test). Six channels of fNIRS were collected from bilateral frontal eye fields and intraparietal sulci (DAN), with right-lateralised channels at the inferior frontal gyrus and temporoparietal junction (VAN). Our first analysis revealed increased G-causality from bottom-up pathways (VAN) during the cold pressor test. However, an equivalent experience of experimental pain during gameplay increased G-causality in top-down (DAN) pathways, with the left intraparietal sulcus serving a hub of connectivity. High game difficulty increased G-causality via top-down pathways and implicated the right inferior frontal gyrus as an interhemispheric hub. Our results are discussed with reference to existing models of both networks and attentional modulation of pain.
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Affiliation(s)
| | - Kellyann Stamp
- School of Computer Science and Mathematics, Liverpool John Moores University, UK
| | - Chelsea Dobbins
- School of Information Technology and Electrical Engineering, The University of Queensland, Australia
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