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Klein F. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications. FRONTIERS IN NEUROERGONOMICS 2024; 5:1286586. [PMID: 38903906 PMCID: PMC11188482 DOI: 10.3389/fnrgo.2024.1286586] [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: 08/31/2023] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
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Affiliation(s)
- Franziska Klein
- Biomedical Devices and Systems Group, R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Neurocognition and Functional Neurorehabilitation Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
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Clemente L, La Rocca M, Paparella G, Delussi M, Tancredi G, Ricci K, Procida G, Introna A, Brunetti A, Taurisano P, Bevilacqua V, de Tommaso M. Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2329. [PMID: 38610540 PMCID: PMC11014209 DOI: 10.3390/s24072329] [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: 03/15/2024] [Revised: 04/03/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.
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Affiliation(s)
- Livio Clemente
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Marianna La Rocca
- Interateneo Department of Fisica ‘M. Merlin’, University of Bari, 70125 Bari, Italy;
- Laboratory of Neuroimaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Giulia Paparella
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Marianna Delussi
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Giusy Tancredi
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Katia Ricci
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Giuseppe Procida
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Alessandro Introna
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Antonio Brunetti
- Electrical and Information Engineering Department, Polytechnic of Bari, 70125 Bari, Italy; (A.B.); (V.B.)
| | - Paolo Taurisano
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Vitoantonio Bevilacqua
- Electrical and Information Engineering Department, Polytechnic of Bari, 70125 Bari, Italy; (A.B.); (V.B.)
| | - Marina de Tommaso
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
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Pellegrino G, Schuler AL, Cai Z, Marinazzo D, Tecchio F, Ricci L, Tombini M, Di Lazzaro V, Assenza G. Assessing cortical excitability with electroencephalography: A pilot study with EEG-iTBS. Brain Stimul 2024; 17:176-183. [PMID: 38286400 DOI: 10.1016/j.brs.2024.01.004] [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: 09/11/2023] [Revised: 11/26/2023] [Accepted: 01/11/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Cortical excitability measures neural reactivity to stimuli, usually delivered via Transcranial Magnetic Stimulation (TMS). Excitation/inhibition balance (E/I) is the ongoing equilibrium between excitatory and inhibitory activity of neural circuits. According to some studies, E/I could be estimated in-vivo and non-invasively through the modeling of electroencephalography (EEG) signals and termed 'intrinsic excitability' measures. Several measures have been proposed (phase consistency in the gamma band, sample entropy, exponent of the power spectral density 1/f curve, E/I index extracted from detrend fluctuation analysis, and alpha power). Intermittent theta burst stimulation (iTBS) of the primary motor cortex (M1) is a non-invasive neuromodulation technique allowing controlled and focal enhancement of TMS cortical excitability and E/I of the stimulated hemisphere. OBJECTIVE Investigating to what extent E/I estimates scale with TMS excitability and how they relate to each other. METHODS M1 excitability (TMS) and several E/I estimates extracted from resting state EEG recordings were assessed before and after iTBS in a cohort of healthy subjects. RESULTS Enhancement of TMS M1 excitability, as measured through motor-evoked potentials (MEPs), and phase consistency of the cortex in high gamma band correlated with each other. Other measures of E/I showed some expected results, but no correlation with TMS excitability measures or strong consistency with each other. CONCLUSIONS EEG E/I estimates offer an intriguing opportunity to map cortical excitability non-invasively, with high spatio-temporal resolution and with a stimulus independent approach. While different EEG E/I estimates may reflect the activity of diverse excitatory-inhibitory circuits, spatial phase synchrony in the gamma band is the measure that best captures excitability changes in the primary motor cortex.
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Affiliation(s)
- Giovanni Pellegrino
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
| | - Anna-Lisa Schuler
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Zhengchen Cai
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies (ISTC) - Consiglio Nazionale Delle Ricerche (CNR), Rome, Italy
| | - Lorenzo Ricci
- UOC Neurologia, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro Del Portillo, 200, 00128, Roma, Italy; UOC Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Unit of Neurology, Neurophysiology, Neurobiology and Psychiatry, Via Alvaro Del Portillo, 21, 00128, Roma, Italy
| | - Mario Tombini
- UOC Neurologia, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro Del Portillo, 200, 00128, Roma, Italy; UOC Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Unit of Neurology, Neurophysiology, Neurobiology and Psychiatry, Via Alvaro Del Portillo, 21, 00128, Roma, Italy
| | - Vincenzo Di Lazzaro
- UOC Neurologia, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro Del Portillo, 200, 00128, Roma, Italy; UOC Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Unit of Neurology, Neurophysiology, Neurobiology and Psychiatry, Via Alvaro Del Portillo, 21, 00128, Roma, Italy
| | - Giovanni Assenza
- UOC Neurologia, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro Del Portillo, 200, 00128, Roma, Italy; UOC Neurology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Unit of Neurology, Neurophysiology, Neurobiology and Psychiatry, Via Alvaro Del Portillo, 21, 00128, Roma, Italy.
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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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Cockx H, Oostenveld R, Tabor M, Savenco E, van Setten A, Cameron I, van Wezel R. fNIRS is sensitive to leg activity in the primary motor cortex after systemic artifact correction. Neuroimage 2023; 269:119880. [PMID: 36693595 DOI: 10.1016/j.neuroimage.2023.119880] [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: 04/29/2022] [Revised: 11/17/2022] [Accepted: 01/13/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool to study cortical activity during movement and gait that requires further validation. This study aimed to assess (1) whether fNIRS can detect the difficult-to-measure leg area of the primary motor cortex (M1) and distinguish it from the hand area; and (2) whether fNIRS can differentiate between automatic (i.e., not requiring one's attention) and non-automatic movement processes. Special attention was attributed to systemic artifacts (i.e., changes in blood pressure, heart rate, breathing) which were assessed and corrected by short channels, i.e., fNIRS channels which are mainly sensitive to superficial scalp hemodynamics. METHODS Twenty-three seated, healthy participants tapped four fingers on a keyboard or tapped the right foot on four squares on the floor in a specific order given by a 12-digit sequence (e.g., 434141243212). Two different sequences were executed: a beforehand learned (i.e., automatic) version and a newly learned (i.e., non-automatic) version. A 36-channel fNIRS device including 12 short channels covered multiple motor-related cortical areas including M1. The fNIRS data were analyzed with a general linear model (GLM). Correlation between the expected functional hemodynamic responses (i.e. task regressor) and the short channels (i.e. nuisance regressors), necessitated performing a separate short channel regression instead of integrating them in the GLM. RESULTS Consistent with the M1 somatotopy, we found significant HbO increases of very large effect size in the lateral M1 channels during finger tapping (Cohen's d = 1.35, p<0.001) and significant HbO increases of moderate effect size in the medial M1 channels during foot tapping (Cohen's d = 0.8, p<0.05). The cortical activity differences between automatic and non-automatic tasks were not significantly different. Importantly, leg movements produced large systemic fluctuations, which were adequately removed by the use of all available short channels. DISCUSSION Our results indicate that fNIRS is sensitive to leg activity in M1, though the sensitivity is lower than for finger activity and requires rigorous correction for systemic fluctuations. We furthermore highlight that systemic artifacts may result in an unreliable GLM analysis when short channels show signals that are similar to the expected hemodynamic responses.
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Affiliation(s)
- Helena Cockx
- Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands.
| | - Robert Oostenveld
- Donders Institute for Brain Cognition and Behaviour, Donders Center for Cognitive Neuroimaging, Radboud University, Kapittelweg 29, 6525EN Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Nobels Väg 9, D2:D235, 17177 Stockholm, Sweden.
| | - Merel Tabor
- Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands
| | - Ecaterina Savenco
- Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands
| | - Arne van Setten
- Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands
| | - Ian Cameron
- Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands; OnePlanet Research Center, Toernooiveld 300, 6525EC Nijmegen, the Netherlands; Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Drienerlolaan 5, 7522NB Enschede, the Netherlands.
| | - Richard van Wezel
- Donders Institute for Brain, Cognition and Behaviour, Biophysics Department, Faculty of Science, Radboud University, Heyendaalseweg 135, 6525AJ Nijmegen, the Netherlands; Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Drienerlolaan 5, 7522NB Enschede, the Netherlands.
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Bonilauri A, Sangiuliano Intra F, Baglio F, Baselli G. Impact of Anatomical Variability on Sensitivity Profile in fNIRS-MRI Integration. SENSORS (BASEL, SWITZERLAND) 2023; 23:2089. [PMID: 36850685 PMCID: PMC9962997 DOI: 10.3390/s23042089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source-detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation.
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Affiliation(s)
- Augusto Bonilauri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
| | | | - Francesca Baglio
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, CADITER, 20148 Milan, Italy
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
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Neuroplasticity Elicited by Modified Pharyngeal Electrical Stimulation: A Pilot Study. Brain Sci 2023; 13:brainsci13010119. [PMID: 36672100 PMCID: PMC9856550 DOI: 10.3390/brainsci13010119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/26/2022] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
Modified pharyngeal electrical stimulation (mPES) is a novel therapeutic method for patients with neurogenic dysphagia and tracheostomy. However, the underlying neural mechanisms are still unclear. This study aims to investigate the impact of mPES on swallowing-related neural networks and involuntary swallowing frequency using functional near-infrared spectroscopy (fNIRS). 20 healthy volunteers participated in this study, including two separate experimental paradigms. Experiment 1: Immediate effect observation, 20 participants (10 female; mean age 47.65 ± 10.48) were delivered with real and sham mPES in random order for 8 repetitions. fNIRS signals were collected during the whole period of Experiments 1. Swallowing frequency was assessed during sham/real mPES. Experiment 2: Prolonged effect observation, 7 out of the 20 participants (4 female; mean age 49.71 ± 6.26) completed real mPES for 5 sessions (1 session/day). 13 of the 20 participants withdrew for personal reasons. Hemodynamic changes were recorded by fNIRS on day 1 and 5. Results show that mPES evoked cortical activation over a distributed network in bilateral primary somatosensory, primary motor, somatosensory association cortex, pre-motor and supplementary motor area, dorsolateral prefrontal cortex, Broca's area, and supramarginal gyrus part of Wernicke's area. Meanwhile, the increased frequency of involuntary swallowing was associated with decreased frontopolar activation (frontopolar cortex: Channel 6, p = 0.024, r = -0.529; Channel 23, p = 0.019, r = -0.545). Furthermore, after five days of mPES, decreased cortical activations were observed in the right dorsolateral prefrontal and supramarginal gyrus part of Wernicke's area, and left frontopolar and M1 areas. Overall, these results might suggest that mPES could elicit changes in neuroplasticity that could reorganize the swallowing-related neural network and increase involuntary swallow frequency.
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Cai Z, Pellegrino G, Lina J, Benali H, Grova C. Hierarchical Bayesian modeling of the relationship between task-related hemodynamic responses and cortical excitability. Hum Brain Mapp 2022; 44:876-900. [PMID: 36250709 PMCID: PMC9875942 DOI: 10.1002/hbm.26107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/10/2022] [Accepted: 09/18/2022] [Indexed: 01/28/2023] Open
Abstract
Investigating the relationship between task-related hemodynamic responses and cortical excitability is challenging because it requires simultaneous measurement of hemodynamic responses while applying noninvasive brain stimulation. Moreover, cortical excitability and task-related hemodynamic responses are both associated with inter-/intra-subject variability. To reliably assess such a relationship, we applied hierarchical Bayesian modeling. This study involved 16 healthy subjects who underwent simultaneous Paired Associative Stimulation (PAS10, PAS25, Sham) while monitoring brain activity using functional Near-Infrared Spectroscopy (fNIRS), targeting the primary motor cortex (M1). Cortical excitability was measured by Motor Evoked Potentials (MEPs), and the motor task-related hemodynamic responses were measured using fNIRS 3D reconstructions. We constructed three models to investigate: (1) PAS effects on the M1 excitability, (2) PAS effects on fNIRS hemodynamic responses to a finger tapping task, and (3) the correlation between PAS effects on M1 excitability and PAS effects on task-related hemodynamic responses. Significant increase in cortical excitability was found following PAS25, whereas a small reduction of the cortical excitability was shown after PAS10 and a subtle increase occurred after sham. Both HbO and HbR absolute amplitudes increased after PAS25 and decreased after PAS10. The probability of the positive correlation between modulation of cortical excitability and hemodynamic activity was 0.77 for HbO and 0.79 for HbR. We demonstrated that PAS stimulation modulates task-related cortical hemodynamic responses in addition to M1 excitability. Moreover, the positive correlation between PAS modulations of excitability and hemodynamics brought insight into understanding the fundamental properties of cortical function and cortical excitability.
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Affiliation(s)
- Zhengchen Cai
- Multimodal Functional Imaging Lab, Department of PhysicsConcordia UniversityMontréalQuébecCanada,PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Giovanni Pellegrino
- Epilepsy Program, Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada,Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Jean‐Marc Lina
- Département de Génie ElectriqueÉcole de Technologie SupérieureMontréalQuébecCanada,Centre De Recherches En MathématiquesMontréalQuébecCanada
| | - Habib Benali
- PERFORM CentreConcordia UniversityMontréalQuébecCanada,Centre De Recherches En MathématiquesMontréalQuébecCanada,Electrical and Computer Engineering Department, Concordia UniversityMontréalCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of PhysicsConcordia UniversityMontréalQuébecCanada,PERFORM CentreConcordia UniversityMontréalQuébecCanada,Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada,Centre De Recherches En MathématiquesMontréalQuébecCanada
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Cai Z, Machado A, Chowdhury RA, Spilkin A, Vincent T, Aydin Ü, Pellegrino G, Lina JM, Grova C. Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean. Sci Rep 2022; 12:2316. [PMID: 35145148 PMCID: PMC8831678 DOI: 10.1038/s41598-022-06082-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40\documentclass[12pt]{minimal}
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\begin{document}$$\text {cm}^2$$\end{document}cm2 along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE.
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Affiliation(s)
- Zhengchen Cai
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.
| | - Alexis Machado
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
| | - Rasheda Arman Chowdhury
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
| | - Amanda Spilkin
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
| | - Thomas Vincent
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.,Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada.,Centre de médecine préventive et d'activité physique, Montréal Heart Institute, Montréal, Canada
| | - Ümit Aydin
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.,MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Giovanni Pellegrino
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Marc Lina
- École de technologie supérieure de l'Université du Québec, Montréal, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada
| | - Christophe Grova
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.,Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada.,Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada
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10
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Vanegas M, Mireles M, Fang Q. MOCA: a systematic toolbox for designing and assessing modular functional near-infrared brain imaging probes. NEUROPHOTONICS 2022; 9:017801. [PMID: 36278785 PMCID: PMC8823693 DOI: 10.1117/1.nph.9.1.017801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 01/11/2022] [Indexed: 05/20/2023]
Abstract
SIGNIFICANCE The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe's performance a daunting task. AIM We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes. APPROACH Our software-modular optode configuration analyzer (MOCA)-implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities. RESULTS Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts. CONCLUSION Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.
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Affiliation(s)
- Morris Vanegas
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Miguel Mireles
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Address all correspondence to Qianqian Fang,
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11
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Cortese AM, Cacciante L, Schuler AL, Turolla A, Pellegrino G. Cortical Thickness of Brain Areas Beyond Stroke Lesions and Sensory-Motor Recovery: A Systematic Review. Front Neurosci 2021; 15:764671. [PMID: 34803596 PMCID: PMC8595399 DOI: 10.3389/fnins.2021.764671] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The clinical outcome of patients suffering from stroke is dependent on multiple factors. The features of the lesion itself play an important role but clinical recovery is remarkably influenced by the plasticity mechanisms triggered by the stroke and occurring at a distance from the lesion. The latter translate into functional and structural changes of which cortical thickness might be easy to quantify one of the main players. However, studies on the changes of cortical thickness in brain areas beyond stroke lesion and their relationship to sensory-motor recovery are sparse. Objectives: To evaluate the effects of cerebral stroke on cortical thickness (CT) beyond the stroke lesion and its association with sensory-motor recovery. Materials and Methods: Five electronic databases (PubMed, Embase, Web of Science, Scopus and the Cochrane Library) were searched. Methodological quality of the included studies was assessed with the Newcastle-Ottawa Scale for non-randomized controlled trials and the Risk of Bias Cochrane tool for randomized controlled trials. Results: The search strategy retrieved 821 records, 12 studies were included and risk of bias assessed. In most of the included studies, cortical thinning was seen at the ipsilesional motor area (M1). Cortical thinning can occur beyond the stroke lesion, typically in regions anatomically connected because of anterograde degeneration. Nonetheless, studies also reported cortical thickening of regions of the unaffected hemisphere, likely related to compensatory plasticity. Some studies revealed a significant correlation between changes in cortical thickness of M1 or somatosensory (S1) cortical areas and motor function recovery. Discussion and Conclusions: Following a stroke, changes in cortical thickness occur both in regions directly connected to the stroke lesion and in contralateral hemisphere areas as well as in the cerebellum. The underlying mechanisms leading to these changes in cortical thickness are still to be fully understood and further research in the field is needed. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020200539; PROSPERO 2020, identifier: CRD42020200539.
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Affiliation(s)
- Anna Maria Cortese
- Laboratory of Rehabilitation Technologies, San Camillo Istituto di Ricovero e Cura a Carattere Scientifico, Venice, Italy
| | - Luisa Cacciante
- Laboratory of Rehabilitation Technologies, San Camillo Istituto di Ricovero e Cura a Carattere Scientifico, Venice, Italy
| | - Anna-Lisa Schuler
- Laboratory of Clinical Imaging and Stimulation, San Camillo Istituto di Ricovero e Cura a Carattere Scientifico, Venice, Italy
| | - Andrea Turolla
- Laboratory of Rehabilitation Technologies, San Camillo Istituto di Ricovero e Cura a Carattere Scientifico, Venice, Italy
| | - Giovanni Pellegrino
- Laboratory of Clinical Imaging and Stimulation, San Camillo Istituto di Ricovero e Cura a Carattere Scientifico, Venice, Italy
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12
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Cai Z, Uji M, Aydin Ü, Pellegrino G, Spilkin A, Delaire É, Abdallah C, Lina J, Grova C. Evaluation of a personalized functional near infra-red optical tomography workflow using maximum entropy on the mean. Hum Brain Mapp 2021; 42:4823-4843. [PMID: 34342073 PMCID: PMC8449120 DOI: 10.1002/hbm.25566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 11/20/2022] Open
Abstract
In the present study, we proposed and evaluated a workflow of personalized near infra-red optical tomography (NIROT) using functional near-infrared spectroscopy (fNIRS) for spatiotemporal imaging of cortical hemodynamic fluctuations. The proposed workflow from fNIRS data acquisition to local 3D reconstruction consists of: (a) the personalized optimal montage maximizing fNIRS channel sensitivity to a predefined targeted brain region; (b) the optimized fNIRS data acquisition involving installation of optodes and digitalization of their positions using a neuronavigation system; and (c) the 3D local reconstruction using maximum entropy on the mean (MEM) to accurately estimate the location and spatial extent of fNIRS hemodynamic fluctuations along the cortical surface. The workflow was evaluated on finger-tapping fNIRS data acquired from 10 healthy subjects for whom we estimated the reconstructed NIROT spatiotemporal images and compared with functional magnetic resonance imaging (fMRI) results from the same individuals. Using the fMRI activation maps as our reference, we quantitatively compared the performance of two NIROT approaches, the MEM framework and the conventional minimum norm estimation (MNE) method. Quantitative comparisons were performed at both single subject and group-level. Overall, our results suggested that MEM provided better spatial accuracy than MNE, while both methods offered similar temporal accuracy when reconstructing oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentration changes evoked by finger-tapping. Our proposed complete workflow was made available in the brainstorm fNIRS processing plugin-NIRSTORM, thus providing the opportunity for other researchers to further apply it to other tasks and on larger populations.
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Affiliation(s)
- Zhengchen Cai
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Makoto Uji
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Giovanni Pellegrino
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Amanda Spilkin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Édouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Chifaou Abdallah
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Jean‐Marc Lina
- Département de Génie ElectriqueÉcole de Technologie SupérieureMontréalQuébecCanada
- Centre De Recherches En MathématiquesMontréalQuébecCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
- Centre De Recherches En MathématiquesMontréalQuébecCanada
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13
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Fu X, Richards JE. devfOLD: a toolbox for designing age-specific fNIRS channel placement. NEUROPHOTONICS 2021; 8:045003. [PMID: 34881349 PMCID: PMC8647945 DOI: 10.1117/1.nph.8.4.045003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 11/09/2021] [Indexed: 05/20/2023]
Abstract
Significance: Near-infrared spectroscopy (NIRS) is a noninvasive technique that uses scalp-placed sensors to measure cerebral hemoglobin concentration. Commercial NIRS instruments do not allow for whole-head coverage and do not intrinsically indicate which brain areas generate the NIRS signal. Hence, the challenge is to design source-detector channel arrangement that maximizes sensitivity to a given brain region of interest (ROI). Existing methods for optimizing channel placement design have been developed using adult head models. Thus, they have limited utility for developmental research. Aim: We aim to build an application from an existing toolbox (fOLD) that guides NIRS channel configuration based on age group, stereotaxic atlas, and ROI (devfOLD). Approach: The devfOLD provides NIRS channel-to-ROI specificity computed using photon propagation simulation with realistic head models from infant, child, and adult age groups. Results: Cortical locations and user-specified specificity cut-off values influence the between-age consistency and differences in the ROI-to-channel correspondence among the example infant and adult age groups. Conclusions: The study highlights the importance of incorporating age-specific head models for optimizing NIRS channel configurations. The devfOLD toolbox is publicly shared and compatible with multiple operating systems.
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Affiliation(s)
- Xiaoxue Fu
- University of South Carolina, Department of Psychology, Columbia, South Carolina, United States
| | - John E. Richards
- University of South Carolina, Department of Psychology, Columbia, South Carolina, United States
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14
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Zhao Y, Xiao X, Jiang Y, Sun P, Zhang Z, Gong Y, Li Z, Zhu C. Transcranial brain atlas-based optimization for functional near-infrared spectroscopy optode arrangement: Theory, algorithm, and application. Hum Brain Mapp 2021; 42:1657-1669. [PMID: 33332685 PMCID: PMC7978141 DOI: 10.1002/hbm.25318] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 11/23/2022] Open
Abstract
The quality of optode arrangement is crucial for group imaging studies when using functional near-infrared spectroscopy (fNIRS). Previous studies have demonstrated the promising effectiveness of using transcranial brain atlases (TBAs), in a manual and intuition-based way, to guide optode arrangement when individual structural MRI data are unavailable. However, the theoretical basis of using TBA to optimize optode arrangement remains unclear, which leads to manual and subjective application. In this study, we first describe the theoretical basis of TBA-based optimization of optode arrangement using a mathematical framework. Second, based on the theoretical basis, an algorithm is proposed for automatically arranging optodes on a virtual scalp. The resultant montage is placed onto the head of each participant guided by a low-cost and portable navigation system. We compared our method with the widely used 10/20-system-assisted optode arrangement procedure, using finger-tapping and working memory tasks as examples of both low- and high-level cognitive systems. Performance, including optode montage designs, locations on each participant's scalp, brain activation, as well as ground truth indices derived from individual MRI data were evaluated. The results give convergent support for our method's ability to provide more accurate, consistent and efficient optode arrangements for fNIRS group imaging than the 10/20 method.
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Affiliation(s)
- Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Xiang Xiao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Neuroimaging Research Branch, National Institute on Drug AbuseNational Institutes of HealthBaltimoreMarylandUSA
| | - Yi‐Han Jiang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Pei‐Pei Sun
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Zong Zhang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Yi‐Long Gong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal University at ZhuhaiZhuhaiChina
- IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina
| | - Chao‐Zhe Zhu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina
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15
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Benitez-Andonegui A, Lührs M, Nagels-Coune L, Ivanov D, Goebel R, Sorger B. Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength. NEUROPHOTONICS 2021; 8:025012. [PMID: 34155480 PMCID: PMC8211086 DOI: 10.1117/1.nph.8.2.025012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/11/2021] [Indexed: 05/20/2023]
Abstract
Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.
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Affiliation(s)
- Amaia Benitez-Andonegui
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
- Maastricht University, Laboratory for Cognitive Robotics and Complex Self-Organizing Systems, Department of Data Science and Knowledge Engineering, Maastricht, The Netherlands
- Address all correspondence to Amaia Benitez-Andonegui,
| | - Michael Lührs
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
| | - Laurien Nagels-Coune
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
| | - Dimo Ivanov
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
| | - Rainer Goebel
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
- Brain Innovation B.V., Research Department, Maastricht, The Netherlands
| | - Bettina Sorger
- Maastricht University, Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Maastricht, The Netherlands
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16
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Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy. Sci Rep 2021; 11:5964. [PMID: 33727581 PMCID: PMC7966407 DOI: 10.1038/s41598-021-85386-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 02/26/2021] [Indexed: 11/28/2022] Open
Abstract
In functional near infrared spectroscopy (fNIRS), deconvolution analysis of oxy and deoxy-hemoglobin concentration changes allows estimating specific hemodynamic response functions (HRF) elicited by neuronal activity, taking advantage of the fNIRS excellent temporal resolution. Diffuse optical tomography (DOT) is also becoming the new standard reconstruction procedure as it is more accurate than the modified Beer Lambert law approach at the sensor level. The objective of this study was to assess the relevance of HRF deconvolution after DOT constrained along the cortical surface. We used local personalized fNIRS montages which consists in optimizing the position of fNIRS optodes to ensure maximal sensitivity to subject specific target brain regions. We carefully evaluated the accuracy of deconvolution when applied after DOT, using realistic simulations involving several HRF models at different signal to noise ratio (SNR) levels and on real data related to motor and visual tasks in healthy subjects and from spontaneous pathological activity in one patient with epilepsy. We demonstrated that DOT followed by deconvolution was able to accurately recover a large variability of HRFs over a large range of SNRs. We found good performances of deconvolution analysis for SNR levels usually encountered in our applications and we were able to reconstruct accurately the temporal dynamics of HRFs in real conditions.
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17
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Cai L, Nitta T, Yokota S, Obata T, Okada E, Kawaguchi H. Targeting brain regions of interest in functional near-infrared spectroscopy-Scalp-cortex correlation using subject-specific light propagation models. Hum Brain Mapp 2021; 42:1969-1986. [PMID: 33621388 PMCID: PMC8046049 DOI: 10.1002/hbm.25367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 12/05/2020] [Accepted: 01/31/2021] [Indexed: 11/11/2022] Open
Abstract
Targeting specific brain regions of interest by the accurate positioning of optodes (emission and detection probes) on the scalp remains a challenge for functional near‐infrared spectroscopy (fNIRS). Since fNIRS data does not provide any anatomical information on the brain cortex, establishing the scalp‐cortex correlation (SCC) between emission‐detection probe pairs on the scalp and the underlying brain regions in fNIRS measurements is extremely important. A conventional SCC is obtained by a geometrical point‐to‐point manner and ignores the effect of light scattering in the head tissue that occurs in actual fNIRS measurements. Here, we developed a sensitivity‐based matching (SBM) method that incorporated the broad spatial sensitivity of the probe pair due to light scattering into the SCC for fNIRS. The SCC was analyzed between head surface fiducial points determined by the international 10–10 system and automated anatomical labeling brain regions for 45 subject‐specific head models. The performance of the SBM method was compared with that of three conventional geometrical matching (GM) methods. We reveal that the light scattering and individual anatomical differences in the head affect the SCC, which indicates that the SBM method is compulsory to obtain the precise SCC. The SBM method enables us to evaluate the activity of cortical regions that are overlooked in the SCC obtained by conventional GM methods. Together, the SBM method could be a promising approach to guide fNIRS users in designing their probe arrangements and in explaining their measurement data.
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Affiliation(s)
- Lin Cai
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Tomonori Nitta
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Sho Yokota
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Takayuki Obata
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Eiji Okada
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan
| | - Hiroshi Kawaguchi
- Department of Electronics and Electrical Engineering, Keio University, Yokohama, Japan.,Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.,Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
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18
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Yücel MA, Lühmann AV, Scholkmann F, Gervain J, Dan I, Ayaz H, Boas D, Cooper RJ, Culver J, Elwell CE, Eggebrecht A, Franceschini MA, Grova C, Homae F, Lesage F, Obrig H, Tachtsidis I, Tak S, Tong Y, Torricelli A, Wabnitz H, Wolf M. Best practices for fNIRS publications. NEUROPHOTONICS 2021; 8:012101. [PMID: 33442557 PMCID: PMC7793571 DOI: 10.1117/1.nph.8.1.012101] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/02/2020] [Indexed: 05/09/2023]
Abstract
The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers.
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Affiliation(s)
- Meryem A. Yücel
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Address all correspondence to Meryem A. Yücel,
| | - Alexander v. Lühmann
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Felix Scholkmann
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Neonatology Research, Zurich, Switzerland
- University of Bern, Institute for Complementary and Integrative Medicine, Bern, Switzerland
| | - Judit Gervain
- Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
- Università di Padova, Department of Social and Developmental Psychology, Padua, Italy
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Applied Cognitive Neuroscience Laboratory, Tokyo, Japan
| | - 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 Psychology, Philadelphia, Pennsylvania, United States
- Drexel University, Drexel Solutions Institute, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Department of Family and Community Health, Philadelphia, Pennsylvania, United States
- Children’s Hospital of Philadelphia, Center for Injury Research and Prevention, Philadelphia, Pennsylvania, United States
| | - David Boas
- Boston University, Neurophotonics Center, Biomedical Engineering, Boston, Massachusetts, United States
| | - Robert J. Cooper
- University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, Biomedical Optics Research Laboratory, London, United Kingdom
| | - Joseph Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Clare E. Elwell
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Adam Eggebrecht
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Maria A. Franceschini
- Massachusetts General Hospital, Harvard Medical School, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Christophe Grova
- Concordia University, Department of Physics and PERFORM Centre, Multimodal Functional Imaging Lab, Montreal, Québec, Canada
- McGill University, Biomedical Engineering Department, Multimodal Functional Imaging Lab, Montreal, Québec, Canada
| | - Fumitaka Homae
- Tokyo Metropolitan University, Department of Language Sciences, Tokyo, Japan
| | - Frédéric Lesage
- Polytechnique Montréal, Department Electrical Engineering, Montreal, Canada
| | - Hellmuth Obrig
- University Hospital Leipzig, Max-Planck-Institute for Human Cognitive and Brain Sciences and Clinic for Cognitive Neurology, Leipzig, Germany
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Sungho Tak
- Korea Basic Science Institute, Research Center for Bioconvergence Analysis, Ochang, Cheongju, Republic of Korea
| | - Yunjie Tong
- Weldon School of Biomedical Engineering Purdue University, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- Consiglio Nazionale delle Ricerche, Istituto di Fotonica e Nanotecnologie, Milan, Italy
| | | | - Martin Wolf
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Neonatology Research, Zurich, Switzerland
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19
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Sharma G, Chowdhury SR. Statistical Analysis to Find out the Optimal Locations for Non Invasive Brain Stimulation. J Med Syst 2020; 44:85. [PMID: 32166505 DOI: 10.1007/s10916-020-1535-7] [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: 04/19/2019] [Accepted: 01/29/2020] [Indexed: 10/24/2022]
Abstract
Non-invasive brain electrical stimulation (NIBES) techniques are progressively used for modulation of neuronal membrane potentials, which alters cortical excitability. The neuronal activity depends on position of channel locations for electrodes and the amount and direction of injected weak current through the target neurons area. In the present paper hybrid near infrared spectroscopy and electroencephalogram (NIRS-EEG) open access dataset for brain computer interface (BCI) has been used to find the best locations for NIBES. The percentage oxygen saturation has been calculated with the help of provided NIRS experimental dataset of changes in concentration of oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) in thirty-six scalp site locations of twenty-eight healthy subjects. The variation in standard deviation have been calculated for given pre-processed EEG signals of thirty locations for same twenty-eight healthy subjects. The statistical one-way ANOVA method has been used to find out the best channels and locations which are having less variation in all motion artifacts. In this method, F value is calculated for these locations and those locations are selected which are significant at 99% confidence interval (P < 0.01). In this study, out of sixty-six locations sixteen best locations have been selected for non-invasive brain electrical stimulation. This pilot study has been used to find out the appropriate locations on the scalp sites to place the electrodes to provide weak direct current stimulation which are less affected by motion artifacts.
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Affiliation(s)
- Gaurav Sharma
- Biomedical Systems Laboratory, Multimedia, Analytics, Networks and Systems Group, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, India.
| | - Shubhajit Roy Chowdhury
- Biomedical Systems Laboratory, Multimedia, Analytics, Networks and Systems Group, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, India
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Jiang Y, Li Z, Zhao Y, Xiao X, Zhang W, Sun P, Yang Y, Zhu C. Targeting brain functions from the scalp: Transcranial brain atlas based on large-scale fMRI data synthesis. Neuroimage 2020; 210:116550. [PMID: 31981781 DOI: 10.1016/j.neuroimage.2020.116550] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/16/2019] [Accepted: 01/14/2020] [Indexed: 12/28/2022] Open
Abstract
Transcranial brain mapping techniques, such as functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS), have been playing an increasingly important role in studies of human brain functions. Given a brain function of interest, fNIRS probes and TMS coils should be properly placed on the scalp to ensure that the function is effectively measured or modulated. However, since brain activity is inside the skull and invisible to the researcher during placement, this blind targeting may cause the device to partially or completely miss the functional target, resulting in inconsistent experimental results and divergent clinical outcomes, especially when participants' structural MRI data are not available. To address this issue, we propose here a framework for targeting a designated function directly from the scalp. First, a functional brain atlas for the targeted brain function is constructed via a meta-analysis of large-scale functional magnetic resonance imaging datasets. Second, the functional brain atlas is presented on the scalp surface by using a transcranial mapping previously established from an structural MRI dataset (n = 114), resulting in a novel functional transcranial brain atlas (fTBA). Finally, a low-cost, portable scalp-navigation system is used to localize the transcranial device on the individual's scalp with the guidance of the fTBA. To demonstrate the feasibility of the targeting framework, both fNIRS and TMS mapping experiments were conducted. The results show that fTBA-guided fNIRS positioning can detect functional activity with high sensitivity and specificity for working memory and motor systems; Moreover, compared with traditional TMS targeting approaches (e.g. the International 10-20 System and the conventional 5-cm rule), the fTBA suggested motor stimulation site is closesr to both the motor hotspot and the center of gravity of motor evoked potentials (MEP-COG). In summary, the proposed method unblinds the transcranial function targeting process using prior information, providing an effective and straightforward approach to transcranial brain mapping studies, especially those without participants' structural MRI data.
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Affiliation(s)
- Yihan Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
| | - Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Wei Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Peipei Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
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Wang L, Ayaz H, Izzetoglu M. Investigation of the source-detector separation in near infrared spectroscopy for healthy and clinical applications. JOURNAL OF BIOPHOTONICS 2019; 12:e201900175. [PMID: 31291506 DOI: 10.1002/jbio.201900175] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/08/2019] [Accepted: 07/09/2019] [Indexed: 05/20/2023]
Abstract
Understanding near infrared light propagation in tissue is vital for designing next generation optical brain imaging devices. Monte Carlo (MC) simulations provide a controlled mechanism to characterize and evaluate contributions of diverse near infrared spectroscopy (NIRS) sensor configurations and parameters. In this study, we developed a multilayer adult digital head model under both healthy and clinical settings and assessed light-tissue interaction through MC simulations in terms of partial differential pathlength, mean total optical pathlength, diffuse reflectance, detector light intensity and spatial sensitivity profile of optical measurements. The model incorporated four layers: scalp, skull, cerebrospinal-fluid and cerebral cortex with and without a customizable lesion for modeling hematoma of different sizes and depths. The effect of source-detector separation (SDS) on optical measurements' sensitivity to brain tissue was investigated. Results from 1330 separate simulations [(4 lesion volumes × 4 lesion depths for clinical +3 healthy settings) × 7 SDS × 10 simulation = 1330)] each with 100 million photons indicated that selection of SDS is critical to acquire optimal measurements from the brain and recommended SDS to be 25 to 35 mm depending on the wavelengths to obtain optical monitoring of the adult brain function. The findings here can guide the design of future NIRS probes for functional neuroimaging and clinical diagnostic systems.
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Affiliation(s)
- Lei Wang
- Drexel University, School of Biomedical Engineering, Science and Health Systems, Philadelphia, Pennsylvania
| | - Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science and Health Systems, Philadelphia, Pennsylvania
- Department of Family and Community Health, University of Pennsylvania, Philadelphia, Pennsylvania
- Children's Hospital of Philadelphia, Center for Injury Research and Prevention, Philadelphia, Pennsylvania
| | - Meltem Izzetoglu
- Villanova University, Electrical and Computer Engineering, Villanova, Pennsylvania
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Pal P, Theisen DL, Datko M, van Lutterveld R, Roy A, Ruf A, Brewer JA. From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems. Clin Neurophysiol 2018; 130:352-358. [PMID: 30669011 DOI: 10.1016/j.clinph.2018.11.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 10/25/2018] [Accepted: 11/22/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system. METHODS A linearly constrained minimum variance beamformer algorithm was used to select the 64 sensors which contributed most highly to the source signal. Monte Carlo-based sampling was then used to randomly generate a large set of reduced 32-sensors montages from the 64 beamformer-selected sensors. The reduced montages were then tested for their ability to reproduce the 128-sensors NF. The high-performing montages were then pooled and analyzed by a k-means clustering machine learning algorithm to produce an optimized reduced 32-sensors montage. RESULTS Nearly 4500 high-performing montages were discovered from the Monte Carlo sampling. After statistically analyzing this pool of high performing montages, a set of refined 32-sensors montages was generated that could reproduce the 128-sensors NF with greater than 80% accuracy for 72% of the test population. CONCLUSION Our Monte Carlo reduction method was used to create reliable reduced-sensors montages which could be used to deliver accurate NF in clinical settings. SIGNIFICANCE A translational pathway is now available by which high-density EEG-based NF measures can be delivered using clinically accessible low-density EEG systems.
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Affiliation(s)
- Prasanta Pal
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA.
| | - Daniel L Theisen
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA
| | - Michael Datko
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA
| | - Remko van Lutterveld
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA
| | - Alexandra Roy
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA
| | - Andrea Ruf
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA
| | - Judson A Brewer
- Center for Mindfulness, University of Massachusetts Medical School, 222 Maple St., Shrewsbury, MA 01545, USA
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Herold F, Wiegel P, Scholkmann F, Müller NG. Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review. J Clin Med 2018; 7:E466. [PMID: 30469482 PMCID: PMC6306799 DOI: 10.3390/jcm7120466] [Citation(s) in RCA: 210] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 11/09/2018] [Accepted: 11/15/2018] [Indexed: 12/18/2022] Open
Abstract
For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.
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Affiliation(s)
- Fabian Herold
- Research Group Neuroprotection, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany.
| | - Patrick Wiegel
- Department of Sport Science, University of Freiburg, Freiburg 79117, Germany.
- Bernstein Center Freiburg, University of Freiburg, Freiburg 79104, Germany.
| | - Felix Scholkmann
- Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zürich, Zürich 8091, Switzerland.
| | - Notger G Müller
- Research Group Neuroprotection, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany.
- Center for Behavioral Brain Sciences (CBBS), Magdeburg 39118, Germany.
- Department of Neurology, Medical Faculty, Otto von Guericke University, Magdeburg 39120, Germany.
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