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Al-Omairi HR, AL-Zubaidi A, Fudickar S, Hein A, Rieger JW. Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique. SENSORS (BASEL, SWITZERLAND) 2024; 24:3173. [PMID: 38794026 PMCID: PMC11125330 DOI: 10.3390/s24103173] [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: 02/22/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.
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Affiliation(s)
- Hayder R. Al-Omairi
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany; (H.R.A.-O.); (A.A.-Z.)
- Department of Biomedical Engineering, University of Technology—Iraq, Baghdad 10066, Iraq
| | - Arkan AL-Zubaidi
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany; (H.R.A.-O.); (A.A.-Z.)
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Sebastian Fudickar
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany; (S.F.); (A.H.)
- Institute for Medical Informatics, University of Lübeck, 23562 Lübeck, Germany
| | - Andreas Hein
- Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany; (S.F.); (A.H.)
| | - Jochem W. Rieger
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany; (H.R.A.-O.); (A.A.-Z.)
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
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2
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Marriot Haresign I, A M Phillips E, V Wass S. Why behaviour matters: Studying inter-brain coordination during child-caregiver interaction. Dev Cogn Neurosci 2024; 67:101384. [PMID: 38657470 PMCID: PMC11059326 DOI: 10.1016/j.dcn.2024.101384] [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: 05/12/2023] [Revised: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024] Open
Abstract
Modern technology allows for simultaneous neuroimaging from interacting caregiver-child dyads. Whereas most analyses that examine the coordination between brain regions within an individual brain do so by measuring changes relative to observed events, studies that examine coordination between two interacting brains generally do this by measuring average intra-brain coordination across entire blocks or experimental conditions. In other words, they do not examine changes in inter-brain coordination relative to individual behavioural events. Here, we discuss the limitations of this approach. First, we present data suggesting that fine-grained temporal interdependencies in behaviour can leave residual artifact in neuroimaging data. We show how artifact can manifest as both power and (through that) phase synchrony effects in EEG and affect wavelet transform coherence in fNIRS analyses. Second, we discuss different possible mechanistic explanations of how inter-brain coordination is established and maintained. We argue that non-event-locked approaches struggle to differentiate between them. Instead, we contend that approaches which examine how interpersonal dynamics change around behavioural events have better potential for addressing possible artifactual confounds and for teasing apart the overlapping mechanisms that drive changes in inter-brain coordination.
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Affiliation(s)
| | | | - Sam V Wass
- Department of Psychology, University of East London, London, UK
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3
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Provost S, Fourdain S, Vannasing P, Tremblay J, Roger K, Caron-Desrochers L, Hüsser A, Paquette N, Doussau A, Poirier N, Simard MN, Gallagher A. Language brain responses and neurodevelopmental outcome in preschoolers with congenital heart disease: A fNIRS study. Neuropsychologia 2024; 196:108843. [PMID: 38423173 DOI: 10.1016/j.neuropsychologia.2024.108843] [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: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024]
Abstract
Neurodevelopmental disabilities affect up to 50% of survivors of congenital heart disease (CHD). Language difficulties are frequently identified during preschool period and can lead to academic, social, behavioral, and emotional difficulties. Structural brain alterations are associated with poorer neurodevelopmental outcomes in patients with CHD during infancy, childhood, and adolescence. However, evidence is lacking about the functional brain activity in children with CHD and its relationship with neurodevelopment. This study therefore aimed to characterize brain responses during a passive story-listening task in 3-year-old children with CHD, and to investigate the relationship between functional brain patterns of language processing and neurodevelopmental outcomes. To do so, we assessed hemodynamic concentration changes, using functional near-infrared spectroscopy (fNIRS), and neurodevelopmental outcomes, using the Wechsler Preschool and Primary Scale of Intelligence - 4th Edition (WPPSI-IV), in children with CHD (n = 19) and healthy controls (n = 23). Compared to their healthy peers, children with CHD had significantly lower scores on the Verbal comprehension index (VCI), the Vocabulary acquisition index (VAI), the General ability index (GAI), and the Information and the Picture Naming subtests of the WPPSI-IV. During the passive story-listening task, healthy controls showed significant hemodynamic brain responses in the temporal and the temporal posterior regions, with stronger activation in the temporal posterior than in the temporal regions. In contrast, children with CHD showed reduced activation in the temporal posterior regions compared to controls, with no difference of activation between regions. Reduced brain responses in the temporal posterior regions were also correlated with lower neurodevelopmental outcomes in both groups. This is the first study that reveals reduced brain functional responses in preschoolers with CHD during a receptive language task. It also suggests that the temporal posterior activation could be a potential brain marker of cognitive development. These findings provide support for the feasibility of identifying brain correlates of neurodevelopmental vulnerabilities in children with CHD.
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Affiliation(s)
- Sarah Provost
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Solène Fourdain
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Phetsamone Vannasing
- Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Julie Tremblay
- Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Kassandra Roger
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Laura Caron-Desrochers
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Alejandra Hüsser
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Natacha Paquette
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Amélie Doussau
- Clinique d'Investigation Neurocardiaque (CINC), Sainte-Justine University Hospital Center, Montréal, QC, Canada
| | - Nancy Poirier
- Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada; Clinique d'Investigation Neurocardiaque (CINC), Sainte-Justine University Hospital Center, Montréal, QC, Canada; Department of Surgery, Division of Cardiac Surgery, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Marie-Noëlle Simard
- Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada; School of Rehabilitation, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Anne Gallagher
- Department of Psychology, Université de Montréal, Montréal, QC, Canada; Research Center, Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.
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4
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Ercan R, Xia Y, Zhao Y, Loureiro R, Yang S, Zhao H. An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2024; 32:763-773. [PMID: 38765316 PMCID: PMC11100859 DOI: 10.1109/tvlsi.2024.3356161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/02/2023] [Accepted: 01/13/2024] [Indexed: 05/22/2024]
Abstract
Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems. We achieved a high classification accuracy of 97.42%, low field-programmable gate array (FPGA) resource utilization of 38354 lookup tables and 6024 flip-flops, as well as low power consumption of 0.021 W in dynamic power. These results outperform conventional CPU support vector machine (SVM) methods and other state-of-the-art SVM implementations. This study has demonstrated that an FPGA-based fNIRS motion artifact classifier can be exploited while meeting low power and resource constraints, which are crucial in embedded hardware systems while keeping high classification accuracy.
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Affiliation(s)
- Renas Ercan
- UCLUCLWC1E 6BTLondonU.K.
- Department of PhysicsUniversity of CambridgeCB2 1TNCambridgeU.K.
| | - Yunjia Xia
- HUB of Intelligent Neuro-Engineering (HUBIN)Division of Surgery and Interventional ScienceUCLWC1E 6BTLondonU.K.
| | - Yunyi Zhao
- HUB of Intelligent Neuro-Engineering (HUBIN)Division of Surgery and Interventional ScienceUCLWC1E 6BTLondonU.K.
| | - Rui Loureiro
- IOMS, Division of Surgery and Interventional Science, UCLWC1E 6BTLondonU.K.
| | - Shufan Yang
- UCLUCLWC1E 6BTLondonU.K.
- Institute of Medical and Biological Engineering, School of Mechanical Engineering, University of LeedsLS2 9JTLeedsU.K.
| | - Hubin Zhao
- HUB of Intelligent Neuro-Engineering (HUBIN)Division of Surgery and Interventional ScienceUCLWC1E 6BTLondonU.K.
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5
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Ning M, Duwadi S, Yücel MA, von Lühmann A, Boas DA, Sen K. fNIRS dataset during complex scene analysis. Front Hum Neurosci 2024; 18:1329086. [PMID: 38576451 PMCID: PMC10991699 DOI: 10.3389/fnhum.2024.1329086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Affiliation(s)
- Matthew Ning
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Sudan Duwadi
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Meryem A. Yücel
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Alexander von Lühmann
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Intelligent Biomedical Sensing (IBS) Lab, Technical University Berlin, Berlin, Germany
| | - David A. Boas
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Kamal Sen
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
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6
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Lyu 吕奕洲 Y, Su 苏紫杉 Z, Neumann D, Meidenbauer KL, Leong 梁元彰 YC. Hostile Attribution Bias Shapes Neural Synchrony in the Left Ventromedial Prefrontal Cortex during Ambiguous Social Narratives. J Neurosci 2024; 44:e1252232024. [PMID: 38316561 PMCID: PMC10904091 DOI: 10.1523/jneurosci.1252-23.2024] [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: 07/06/2023] [Revised: 12/20/2023] [Accepted: 01/07/2024] [Indexed: 02/07/2024] Open
Abstract
Hostile attribution bias refers to the tendency to interpret social situations as intentionally hostile. While previous research has focused on its developmental origins and behavioral consequences, the underlying neural mechanisms remain underexplored. Here, we employed functional near-infrared spectroscopy (fNIRS) to investigate the neural correlates of hostile attribution bias. While undergoing fNIRS, male and female participants listened to and provided attribution ratings for 21 hypothetical scenarios where a character's actions resulted in a negative outcome for the listener. Ratings of hostile intentions were averaged to measure hostile attribution bias. Using intersubject representational similarity analysis, we found that participants with similar levels of hostile attribution bias exhibited higher levels of neural synchrony during narrative listening, suggesting shared interpretations of the scenarios. This effect was localized to the left ventromedial prefrontal cortex (VMPFC) and was particularly prominent in scenarios where the character's intentions were highly ambiguous. We then grouped participants into high and low bias groups based on a median split of their hostile attribution bias scores. A similarity-based classifier trained on the neural data classified participants as having high or low bias with 75% accuracy, indicating that the neural time courses during narrative listening was systematically different between the two groups. Furthermore, hostile attribution bias correlated negatively with attributional complexity, a measure of one's tendency to consider multifaceted causes when explaining behavior. Our study sheds light on the neural mechanisms underlying hostile attribution bias and highlights the potential of using fNIRS to develop nonintrusive and cost-effective neural markers of this sociocognitive bias.
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Affiliation(s)
- Yizhou Lyu 吕奕洲
- Department of Psychology, University of Chicago, Chicago 60637, Illinois
| | - Zishan Su 苏紫杉
- Department of Psychology, University of Chicago, Chicago 60637, Illinois
| | - Dawn Neumann
- Department of Physical Medicine and Rehabilitation, Indiana University School of Medicine, Indianapolis 46202, Indiana
| | | | - Yuan Chang Leong 梁元彰
- Department of Psychology, University of Chicago, Chicago 60637, Illinois
- Neuroscience Institute, The University of Chicago, Chicago 60637, Illinois
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7
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Ning M, Duwadi S, Yücel MA, Von Lühmann A, Boas DA, Sen K. fNIRS Dataset During Complex Scene Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576715. [PMID: 38328139 PMCID: PMC10849700 DOI: 10.1101/2024.01.23.576715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis (CSA). Here, we investigated capability of functional near-infrared spectroscopy (fNIRS) to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. To date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet. This report provides an open-access fNIRS dataset that can be used to develop, test, and compare machine learning algorithms for classifying attended locations based on the fNIRS signals on a single trial basis.
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Affiliation(s)
- Matthew Ning
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sudan Duwadi
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Meryem A. Yücel
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Alexander Von Lühmann
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
- Intelligent Biomedical Sensing (IBS) Lab, Technische Universität Berlin, 10587 Berlin, Germany
| | - David A. Boas
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Kamal Sen
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
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8
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Healey R, Goldsworthy M, Salomoni S, Weber S, Kemp S, Hinder MR, St George RJ. Impaired motor inhibition during perceptual inhibition in older, but not younger adults: a psychophysiological study. Sci Rep 2024; 14:2023. [PMID: 38263414 PMCID: PMC10805883 DOI: 10.1038/s41598-024-52269-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/16/2024] [Indexed: 01/25/2024] Open
Abstract
The prefrontal cortex (PFC) governs the ability to rapidly cancel planned movements when no longer appropriate (motor inhibition) and ignore distracting stimuli (perceptual inhibition). It is unclear to what extent these processes interact, and how they are impacted by age. The interplay between perceptual and motor inhibition was investigated using a Flanker Task, a Stop Signal Task and a combined Stop Signal Flanker Task in healthy young (n = 33, Mean = 24 years) and older adults (n = 32, Mean = 71 years). PFC activity was measured with functional near-infrared spectroscopy (fNIRS), while electromyography (EMG) measured muscle activity in the fingers used to respond to the visual cues. Perceptual inhibition (the degree to which incongruent flankers slowed response time to a central cue) and motor inhibition (the speed of cancellation of EMG activation following stop cues) independently declined with age. When both processes were engaged together, PFC activity increased for both age groups, however only older adults exhibited slower motor inhibition. The results indicate that cortical upregulation was sufficient to compensate for the increased task demands in younger but not older adults, suggesting potential resource sharing and neural limitations particularly in older adults.
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Affiliation(s)
- Rebecca Healey
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Megan Goldsworthy
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Sauro Salomoni
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Simon Weber
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Sarah Kemp
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
- Integrative Model-Based Cognitive Neuroscience Research Unit, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Mark R Hinder
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Rebecca J St George
- Sensorimotor Neuroscience and Ageing Research Laboratory, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia.
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Fourdain S, Provost S, Tremblay J, Vannasing P, Doussau A, Caron-Desrochers L, Gaudet I, Roger K, Hüsser A, Dehaes M, Martinez-Montes E, Poirier N, Gallagher A. Functional brain connectivity after corrective cardiac surgery for critical congenital heart disease: a preliminary near-infrared spectroscopy (NIRS) report. Child Neuropsychol 2023; 29:1088-1108. [PMID: 36718095 DOI: 10.1080/09297049.2023.2170340] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 01/13/2023] [Indexed: 02/01/2023]
Abstract
Patients with congenital heart disease (CHD) requiring cardiac surgery in infancy are at high risk for neurodevelopmental impairments. Neonatal imaging studies have reported disruptions of brain functional organization before surgery. Yet, the extent to which functional network alterations are present after cardiac repair remains unexplored. This preliminary study aimed at investigating cortical functional connectivity in 4-month-old infants with repaired CHD, using resting-state functional near-infrared spectroscopy (fNIRS). After fNIRS signal frequency decomposition, we compared values of magnitude-squared coherence as a measure of connectivity strength, between 21 infants with corrected CHD and 31 healthy controls. We identified a subset of connections with differences between groups at an uncorrected statistical level of p < .05 while controlling for sex and maternal socioeconomic status, with most of these connections showing reduced connectivity in infants with CHD. Although none of these differences reach statistical significance after FDR correction, likely due to the small sample size, moderate to large effect sizes were found for group-differences. If replicated, these results would therefore suggest preliminary evidence that alterations of brain functional connectivity are present in the months after cardiac surgery. Additional studies involving larger sample size are needed to replicate our data, and comparisons between pre- and postoperative findings would allow to further delineate alterations of functional brain connectivity in this population.
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Affiliation(s)
- Solène Fourdain
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Sarah Provost
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Julie Tremblay
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | | | - Amélie Doussau
- Clinique d'investigation neurocardiaque (CINC), Sainte-Justine, Montreal University Hospital Center, Montreal, QC, Canada
| | - Laura Caron-Desrochers
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Isabelle Gaudet
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Kassandra Roger
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Alejandra Hüsser
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Mathieu Dehaes
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Montreal, QC, Canada
| | | | - Nancy Poirier
- Clinique d'investigation neurocardiaque (CINC), Sainte-Justine, Montreal University Hospital Center, Montreal, QC, Canada
- Department of Surgery, Faculty of Medicine, Université de Montreal, Montreal, QC, Canada
| | - Anne Gallagher
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
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10
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Rico-Picó J, Moyano S, Conejero Á, Hoyo Á, Ballesteros-Duperón MÁ, Rueda MR. Early development of electrophysiological activity: Contribution of periodic and aperiodic components of the EEG signal. Psychophysiology 2023; 60:e14360. [PMID: 37322838 DOI: 10.1111/psyp.14360] [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: 10/03/2022] [Revised: 05/04/2023] [Accepted: 05/10/2023] [Indexed: 06/17/2023]
Abstract
Brain function rapidly changes in the first 2 years of life. In the last decades, resting-state EEG has been widely used to explore those changes. Previous studies have focused on the relative power of the signal in established frequency bands (i.e., theta, alpha, and beta). However, EEG power is a mixture of a 1/f-like background power (aperiodic) in combination with narrow peaks that appear over that curve (periodic activity, e.g., alpha peak). Therefore, it is possible that relative power captures both, aperiodic and periodic brain activity, contributing to changes in electrophysiological activity observed in infancy. For this reason, we explored the early developmental trajectory of the relative power in theta, alpha, and beta frequency bands from infancy to toddlerhood and compared it with changes in periodic activity in a longitudinal study with three waves at age 6, 9, and 16 to 18 months. Finally, we tested the contribution of periodic activity and aperiodic components of the EEG to age changes in relative power. We found that relative power and periodic activity trajectories differed in this period in all the frequency bands but alpha. Furthermore, aperiodic EEG activity flattened between 6 and 18 months. More importantly, only alpha relative power was exclusively related to periodic activity, whereas aperiodic components of the signal significantly contributed to the relative power of activity in theta and beta bands. Thus, relative power in these frequencies is influenced by developmental changes of the aperiodic activity, which should be considered for future studies.
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Affiliation(s)
- Josué Rico-Picó
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - Sebastián Moyano
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - Ángela Conejero
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
- Department of Developmental and Educational Psychology, University of Granada, Granada, Spain
| | - Ángela Hoyo
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | - M Ángeles Ballesteros-Duperón
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
- Department of Psychobiology, University of Granada, Granada, Spain
| | - M Rosario Rueda
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
- Department of Experimental Psychology, University of Granada, Granada, Spain
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11
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Bonzano L, Biggio M, Brigadoi S, Pedullà L, Pagliai M, Iester C, Brichetto G, Cutini S, Bove M. Don't plan, just do it: Cognitive and sensorimotor contributions to manual dexterity. Neuroimage 2023; 280:120348. [PMID: 37625501 DOI: 10.1016/j.neuroimage.2023.120348] [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: 05/02/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 08/27/2023] Open
Abstract
Manual dexterity is referred to as the skill to perform fine motor movements and it has been assumed to be associated to the cognitive domain, as well as the sensorimotor one. In this work, we investigated with functional near-infrared spectroscopy the cortical activations elicited by the execution of the 9-HPT, i.e., a standard test evaluating manual dexterity in which nine pegs were taken, placed into and then removed from nine holes on a board as quickly as possible. For comparison, we proposed a new active control task mainly involving the sensorimotor domain, in which the pegs must be placed and removed using the same single hole (1-HPT). Behaviorally, we found two distinct groups based on the difference between the execution time of the 9-HPT and the 1-HPT (ΔHPT). Cortical areas belonging to the network controlling reaching and grasping movements were active in both groups; however, participants showing a large ΔHPT presented significantly higher activation in prefrontal cortical areas (right BA10 and BA11) during 9-HPT and 1-HPT performance with respect to the participants with a small ΔHPT, who showed a deactivation in BA10. Unexpectedly, we observed a significant linear relationship between ΔHPT and right BA10 activity. This suggested that participants performing the 9-HPT more slowly than the 1-HPT recruited prefrontal areas implicitly exploiting the cognitive skills of planning, perhaps in search of a motor strategy to solve the test activating attentional and cognitive control processes, but this resulted not efficient and instead increased the time to accomplish a manual dexterity task.
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Affiliation(s)
- Laura Bonzano
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Monica Biggio
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Sabrina Brigadoi
- Department of Developmental and Social Psychology, University of Padova, Via Venezia, 8, Padua 35131, Italy
| | - Ludovico Pedullà
- Italian Multiple Sclerosis Foundation, Scientific Research Area, Genoa, Italy
| | | | - Costanza Iester
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Giampaolo Brichetto
- Italian Multiple Sclerosis Foundation, Scientific Research Area, Genoa, Italy
| | - Simone Cutini
- Department of Developmental and Social Psychology, University of Padova, Via Venezia, 8, Padua 35131, Italy.
| | - Marco Bove
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy.
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12
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Azimzadeh K, Barekatain M, Tabibian F. Application of Functional Near-Infrared Spectroscopy in Apraxia Studies in Alzheimer's Disease: A Proof of Concept Experiment. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023; 13:319-322. [PMID: 37809017 PMCID: PMC10559297 DOI: 10.4103/jmss.jmss_40_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 09/10/2022] [Accepted: 09/19/2022] [Indexed: 10/10/2023]
Affiliation(s)
- Kiarash Azimzadeh
- Departments of Psychiatry, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Majid Barekatain
- Departments of Psychiatry, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Farinaz Tabibian
- Departments of Psychiatry, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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13
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Al-Omairi HR, Fudickar S, Hein A, Rieger JW. Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement. SENSORS (BASEL, SWITZERLAND) 2023; 23:3979. [PMID: 37112320 PMCID: PMC10146128 DOI: 10.3390/s23083979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 06/19/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical non-invasive neuroimaging technique that allows participants to move relatively freely. However, head movements frequently cause optode movements relative to the head, leading to motion artifacts (MA) in the measured signal. Here, we propose an improved algorithmic approach for MA correction that combines wavelet and correlation-based signal improvement (WCBSI). We compare its MA correction accuracy to multiple established correction approaches (spline interpolation, spline-Savitzky-Golay filter, principal component analysis, targeted principal component analysis, robust locally weighted regression smoothing filter, wavelet filter, and correlation-based signal improvement) on real data. Therefore, we measured brain activity in 20 participants performing a hand-tapping task and simultaneously moving their head to produce MAs at different levels of severity. In order to obtain a "ground truth" brain activation, we added a condition in which only the tapping task was performed. We compared the MA correction performance among the algorithms on four predefined metrics (R, RMSE, MAPE, and ΔAUC) and ranked the performances. The suggested WCBSI algorithm was the only one exceeding average performance (p < 0.001), and it had the highest probability to be the best ranked algorithm (78.8% probability). Together, our results indicate that among all algorithms tested, our suggested WCBSI approach performed consistently favorably across all measures.
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Affiliation(s)
- Hayder R. Al-Omairi
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, D-26129 Oldenburg, Germany
- Department of Biomedical Engineering, University of Technology—Iraq, Baghdad 10066, Iraq
| | - Sebastian Fudickar
- Assistance Systems and Medical Device Technology, Carl von Ossietzky Universität Oldenburg, D-26111 Oldenburg, Germany; (S.F.); (A.H.)
- Institute for Medical Informatics, University of Lübeck, D-23538 Lübeck, Germany
| | - Andreas Hein
- Assistance Systems and Medical Device Technology, Carl von Ossietzky Universität Oldenburg, D-26111 Oldenburg, Germany; (S.F.); (A.H.)
| | - Jochem W. Rieger
- Applied Neurocognitive Psychology Lab, Carl von Ossietzky Universität Oldenburg, D-26129 Oldenburg, Germany
- Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, D-26129 Oldenburg, Germany
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14
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Patashov D, Menahem Y, Gurevitch G, Kameda Y, Goldstein D, Balberg M. fNIRS: Non-stationary preprocessing methods. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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15
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Alexopoulos J, Giordano V, Doering S, Seidl R, Benavides-Varela S, Russwurm M, Greenwood S, Berger A, Bartha-Doering L. Sex differences in neural processing of speech in neonates. Cortex 2022; 157:117-128. [PMID: 36279755 DOI: 10.1016/j.cortex.2022.09.007] [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: 02/02/2022] [Revised: 05/24/2022] [Accepted: 09/04/2022] [Indexed: 12/15/2022]
Abstract
The large majority of studies shows that girls develop their language skills faster than boys in the first few years of life. Are girls born with this advantage in language development? The present study used fNIRS in neonates to investigate sex differences in neural processing of speech within the first days of life. We found that speech stimuli elicited significantly more brain activity than non-speech stimuli in both groups of male and female neonates. However, whereas girls showed significant HbO changes to speech stimuli only within the left hemisphere, boys exhibited simultaneous neural activations in both hemispheres, with a larger and more significant fronto-temporal cluster in the right hemisphere. Furthermore, in boys, the variation in time-to-peak latencies was considerably greater than in girls. These findings suggest an earlier maturation of language-related brain areas in girls and highlight the importance of sex-specific investigations of neural language networks in infants.
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Affiliation(s)
- Johanna Alexopoulos
- Department of Psychoanalysis and Psychotherapy, Medical University of Vienna, Vienna, Austria; Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Vito Giordano
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Stephan Doering
- Department of Psychoanalysis and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rainer Seidl
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Silvia Benavides-Varela
- Department of Developmental Psychology and Socialization & Department of Neuroscience, University of Padova, Padova, Italy
| | - Magdalena Russwurm
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Stephanie Greenwood
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Angelika Berger
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Lisa Bartha-Doering
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria.
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16
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Huang R, Hong KS, Yang D, Huang G. Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review. Front Neurosci 2022; 16:878750. [PMID: 36263362 PMCID: PMC9576156 DOI: 10.3389/fnins.2022.878750] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/04/2022] Open
Abstract
With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.
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Affiliation(s)
- Ruisen Huang
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
- *Correspondence: Keum-Shik Hong,
| | - Dalin Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Guanghao Huang
- Institute for Future, School of Automation, Qingdao University, Qingdao, China
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17
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Hüsser A, Caron-Desrochers L, Tremblay J, Vannasing P, Martínez-Montes E, Gallagher A. Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data. NEUROPHOTONICS 2022; 9:045004. [PMID: 36405999 PMCID: PMC9665873 DOI: 10.1117/1.nph.9.4.045004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
SIGNIFICANCE Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields. AIM We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength). APPROACH We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions. RESULTS PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics. CONCLUSIONS This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.
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Affiliation(s)
- Alejandra Hüsser
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
- Université de Montréal, Department of Psychology, Montréal, Quebec, Canada
| | - Laura Caron-Desrochers
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
- Université de Montréal, Department of Psychology, Montréal, Quebec, Canada
| | - Julie Tremblay
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
| | - Phetsamone Vannasing
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
| | | | - Anne Gallagher
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
- Université de Montréal, Department of Psychology, Montréal, Quebec, Canada
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18
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Gao Y, Chao H, Cavuoto L, Yan P, Kruger U, Norfleet JE, Makled BA, Schwaitzberg S, De S, Intes X. Deep learning-based motion artifact removal in functional near-infrared spectroscopy. NEUROPHOTONICS 2022; 9:041406. [PMID: 35475257 PMCID: PMC9034734 DOI: 10.1117/1.nph.9.4.041406] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 03/10/2022] [Indexed: 06/01/2023]
Abstract
Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.
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Affiliation(s)
- Yuanyuan Gao
- Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States
| | - Hanqing Chao
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Lora Cavuoto
- University at Buffalo, Department of Industrial and Systems Engineering, Buffalo, New York, United States
| | - Pingkun Yan
- Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Uwe Kruger
- Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Jack E. Norfleet
- U.S. Army Combat Capabilities Development Command–Soldier Center, Orlando, Florida, United States
- SFC Paul Ray Smith Simulation and Training Technology Center, Orlando, Florida, United States
- Medical Simulation Research Branch, Orlando, Florida, United States
| | - Basiel A. Makled
- U.S. Army Combat Capabilities Development Command–Soldier Center, Orlando, Florida, United States
- SFC Paul Ray Smith Simulation and Training Technology Center, Orlando, Florida, United States
- Medical Simulation Research Branch, Orlando, Florida, United States
| | - Steven Schwaitzberg
- University at Buffalo, Department of Surgery, Buffalo, New York, United States
| | - Suvranu De
- Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Xavier Intes
- Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging in Medicine, Troy, New York, United States
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
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19
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Yang M, Xia M, Zhang S, Wu D, Li D, Hou X, Wang D. Motion artifact correction for resting-state neonatal functional near-infrared spectroscopy through adaptive estimation of physiological oscillation denoising. NEUROPHOTONICS 2022; 9:045002. [PMID: 36284541 PMCID: PMC9587758 DOI: 10.1117/1.nph.9.4.045002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
SIGNIFICANCE Functional near-infrared spectroscopy (fNIRS) for resting-state neonatal brain function evaluation provides assistance for pediatricians in diagnosis and monitoring treatment outcomes. Artifact contamination is an important challenge in the application of fNIRS in the neonatal population. AIM Our study aims to develop a correction algorithm that can effectively remove different types of artifacts from neonatal data. APPROACH In the study, we estimate the recognition threshold based on the amplitude characteristics of the signal and artifacts. After artifact recognition, Spline and Gaussian replacements are used separately to correct the artifacts. Various correction method recovery effects on simulated artifact and actual neonatal data are compared using the Pearson correlation ( R ) and root mean square error (RMSE). Simulated data connectivity recovery is used to compare various method performances. RESULTS The neonatal resting-state data corrected by our method showed better agreement with results by visual recognition and correction, and significant improvements ( R = 0.732 ± 0.155 , RMSE = 0.536 ± 0.339 ; paired t -test, ** p < 0.01 ). Moreover, the method showed a higher degree of recovery of connectivity in simulated data. CONCLUSIONS The proposed algorithm corrects artifacts such as baseline shifts, spikes, and serial disturbances in neonatal fNIRS data quickly and more effectively. It can be used for preprocessing in clinical applications of neonatal fNIRS brain function detection.
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Affiliation(s)
- Mingxi Yang
- Beihang University, Ministry of Education, School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Beijing, China
| | - Meiyun Xia
- Beihang University, School of Mechanical Engineering and Automation, State Key Laboratory of Virtual Reality Technology and System, Beijing, China
| | - Shen Zhang
- Beihang University, Ministry of Education, School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Beijing, China
| | - Di Wu
- Beihang University, Ministry of Education, School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Beijing, China
| | - Deyu Li
- Beihang University, Ministry of Education, School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Beijing, China
- Beihang University, School of Mechanical Engineering and Automation, State Key Laboratory of Virtual Reality Technology and System, Beijing, China
| | - Xinlin Hou
- Peking University First Hospital, Department of Neonatal Ward, Beijing, China
| | - Daifa Wang
- Beihang University, Ministry of Education, School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory of Biomechanics and Mechanobiology, Beijing, China
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20
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Li Z, Hong B, Wang D, Nolte G, Engel AK, Zhang D. Speaker-listener neural coupling reveals a right-lateralized mechanism for non-native speech-in-noise comprehension. Cereb Cortex 2022; 33:3701-3714. [PMID: 35975617 DOI: 10.1093/cercor/bhac302] [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: 06/03/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 11/14/2022] Open
Abstract
While the increasingly globalized world has brought more and more demands for non-native language communication, the prevalence of background noise in everyday life poses a great challenge to non-native speech comprehension. The present study employed an interbrain approach based on functional near-infrared spectroscopy (fNIRS) to explore how people adapt to comprehend non-native speech information in noise. A group of Korean participants who acquired Chinese as their non-native language was invited to listen to Chinese narratives at 4 noise levels (no noise, 2 dB, -6 dB, and - 9 dB). These narratives were real-life stories spoken by native Chinese speakers. Processing of the non-native speech was associated with significant fNIRS-based listener-speaker neural couplings mainly over the right hemisphere at both the listener's and the speaker's sides. More importantly, the neural couplings from the listener's right superior temporal gyrus, the right middle temporal gyrus, as well as the right postcentral gyrus were found to be positively correlated with their individual comprehension performance at the strongest noise level (-9 dB). These results provide interbrain evidence in support of the right-lateralized mechanism for non-native speech processing and suggest that both an auditory-based and a sensorimotor-based mechanism contributed to the non-native speech-in-noise comprehension.
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Affiliation(s)
- Zhuoran Li
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China.,Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Bo Hong
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China.,Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Daifa Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg Eppendorf, 20246 Hamburg, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg Eppendorf, 20246 Hamburg, Germany
| | - Dan Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China.,Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
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21
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Ayaz H, Baker WB, Blaney G, Boas DA, Bortfeld H, Brady K, Brake J, Brigadoi S, Buckley EM, Carp SA, Cooper RJ, Cowdrick KR, Culver JP, Dan I, Dehghani H, Devor A, Durduran T, Eggebrecht AT, Emberson LL, Fang Q, Fantini S, Franceschini MA, Fischer JB, Gervain J, Hirsch J, Hong KS, Horstmeyer R, Kainerstorfer JM, Ko TS, Licht DJ, Liebert A, Luke R, Lynch JM, Mesquida J, Mesquita RC, Naseer N, Novi SL, Orihuela-Espina F, O’Sullivan TD, Peterka DS, Pifferi A, Pollonini L, Sassaroli A, Sato JR, Scholkmann F, Spinelli L, Srinivasan VJ, St. Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report. NEUROPHOTONICS 2022; 9:S24001. [PMID: 36052058 PMCID: PMC9424749 DOI: 10.1117/1.nph.9.s2.s24001] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, Pennsylvania, United States
- Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Heather Bortfeld
- University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, California, United States
| | - Kenneth Brady
- Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, Illinois, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - Sabrina Brigadoi
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
| | - Erin M. Buckley
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
| | - Kyle R. Cowdrick
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Joseph P. Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Tokyo, Japan
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Anna Devor
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Turgut Durduran
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain
| | - Adam T. Eggebrecht
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Lauren L. Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Jonas B. Fischer
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Keum-Shik Hong
- Pusan National University, School of Mechanical Engineering, Busan, Republic of Korea
- Qingdao University, School of Automation, Institute for Future, Qingdao, China
| | - Roarke Horstmeyer
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Tiffany S. Ko
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Daniel J. Licht
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Adam Liebert
- Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
| | - Robert Luke
- Macquarie University, Department of Linguistics, Sydney, New South Wales, Australia
- Macquarie University Hearing, Australia Hearing Hub, Sydney, New South Wales, Australia
| | - Jennifer M. Lynch
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Jaume Mesquida
- Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Noman Naseer
- Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
| | - Sergio L. Novi
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | | | - Thomas D. O’Sullivan
- University of Notre Dame, Department of Electrical Engineering, Notre Dame, Indiana, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
| | | | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - João Ricardo Sato
- Federal University of ABC, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
| | - Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Lorenzo Spinelli
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Vivek J. Srinivasan
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- NYU Langone Health, Department of Ophthalmology, New York, New York, United States
- NYU Langone Health, Department of Radiology, New York, New York, United States
| | - Keith St. Lawrence
- Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
- Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Tara Urner
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Heidrun Wabnitz
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Martin Wolf
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Shiqi Xu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Changhuei Yang
- California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wenjun Zhou
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China
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22
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Chajes JR, Stern JA, Kelsey CM, Grossmann T. Examining the Role of Socioeconomic Status and Maternal Sensitivity in Predicting Functional Brain Network Connectivity in 5-Month-Old Infants. Front Neurosci 2022; 16:892482. [PMID: 35757535 PMCID: PMC9226752 DOI: 10.3389/fnins.2022.892482] [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: 03/09/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Infancy is a sensitive period of human brain development that is plastically shaped by environmental factors. Both proximal factors, such as sensitive parenting, and distal factors, such as socioeconomic status (SES), are known predictors of individual differences in structural and functional brain systems across the lifespan, yet it is unclear how these familial and contextual factors work together to shape functional brain development during infancy, particularly during the first months of life. In the current study, we examined pre-registered hypotheses regarding the interplay between these factors to assess how maternal sensitivity, within the broader context of socioeconomic variation, relates to the development of functional connectivity in long-range cortical brain networks. Specifically, we measured resting-state functional connectivity in three cortical brain networks (fronto-parietal network, default mode network, homologous-interhemispheric connectivity) using functional near-infrared spectroscopy (fNIRS), and examined the associations between maternal sensitivity, SES, and functional connectivity in a sample of 5-month-old infants and their mothers (N = 50 dyads). Results showed that all three networks were detectable during a passive viewing task, and that maternal sensitivity was positively associated with functional connectivity in the default mode network, such that infants with more sensitive mothers exhibited enhanced functional connectivity in this network. Contrary to hypotheses, we did not observe any associations of SES with functional connectivity in the brain networks assessed in this study. This suggests that at 5 months of age, maternal sensitivity is an important proximal environmental factor associated with individual differences in functional connectivity in a long-range cortical brain network implicated in a host of emotional and social-cognitive brain processes.
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Affiliation(s)
- Johanna R Chajes
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - Jessica A Stern
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - Caroline M Kelsey
- Department of Psychology, University of Virginia, Charlottesville, VA, United States.,Division of Developmental Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Tobias Grossmann
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
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23
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Study on the Effect of Different Transcranial Pulse Current Stimulation Intervention Programs for Eliminating Physical Fatigue. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Previous studies have reported the effect of transcranial pulsed current stimulation (tPCS) on eliminating cognitive fatigue, but there is little research on optimizing the intervention program of tPCS. The purpose of this study was to explore the effect of different tPCS intervention programs on the elimination of physical fatigue in college athletes. Accordingly, 40 healthy college athletes were randomly divided into two groups of 20, denoted as A and B. Both groups exercised on treadmills. There were 15 subjects in group A who met the criteria of moderate physical fatigue, and 15 subjects in group B who met the criteria of severe physical fatigue. The subjects in each group were intervened with five different intervention programs of tPCS (intervention programs I, II, III, IV and V). The heart rate variability (HRV) and concentrations of oxygenated hemoglobin (HbO2) were measured before and after each intervention to judge the elimination effects of different intervention programs on different degrees of physical fatigue; the measurement indicators of the HRV include RMSSD, SDNN, HF and LF. The results indicated that tPCS intervention can eliminate both moderate and severe physical fatigue. Programs II, III, and IV had a significant effect on eliminating the moderate physical fatigue of athletes (p < 0.05), among which program II, with a stimulation time of 30 min and a stimulation intensity of sensory intensity, had the best effect. Programs I, II, III, and IV all had significant effects on eliminating the severe physical fatigue of athletes (p < 0.05), among which program I, with a stimulation time of 30 min and a stimulation intensity of sensory intensity + 0.2 mA, had the best effect. We conclude that different tPCS intervention programs can have different effects on the elimination of physical fatigue. The effects of the five intervention programs on the elimination of physical fatigue in athletes are as follows: program II is most suitable for moderate physical fatigue, and program I is most suitable for severe physical fatigue.
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24
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Hakim U, Pinti P, Noah AJ, Zhang X, Burgess P, Hamilton A, Hirsch J, Tachtsidis I. Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal. NEUROPHOTONICS 2022; 9:025001. [PMID: 35599691 PMCID: PMC9116886 DOI: 10.1117/1.nph.9.2.025001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 04/13/2022] [Indexed: 06/15/2023]
Abstract
Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated (HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus onHbO 2 only. Previous work has shown thatHbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using bothHbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination ofHbO 2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combineHbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives onHbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of usingHbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.
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Affiliation(s)
- Uzair Hakim
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Paola Pinti
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- University of London, Birkbeck College, Centre for Brain and Cognitive Development, London, United Kingdom
| | - Adam J. Noah
- Yale University, Department of Neuroscience and Comparative Medicine, Yale School of Medicine, United States
| | - Xian Zhang
- Yale University, Department of Neuroscience and Comparative Medicine, Yale School of Medicine, United States
| | - Paul Burgess
- University College London, Institute of Cognitive Neuroscience, London, United Kingdom
| | - Antonia Hamilton
- University College London, Institute of Cognitive Neuroscience, London, United Kingdom
| | - Joy Hirsch
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- Yale University, Department of Neuroscience and Comparative Medicine, Yale School of Medicine, United States
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
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25
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Kim M, Lee S, Dan I, Tak S. A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS. J Neural Eng 2022; 19. [PMID: 35038682 DOI: 10.1088/1741-2552/ac4bfc] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 01/17/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN). APPROACH The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts. MAIN RESULTS Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contains variants of shapes and amplitudes of HRF. SIGNIFICANCE The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.
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Affiliation(s)
- MinWoo Kim
- School of Biomedical Convergence Engineering, Pusan National University, 49 Busandaehak-ro, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, Yangsan, 50612, Korea (the Republic of)
| | - Seonjin Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, 162 Yeongudanji-ro, Cheongwon-gu, Ochang-eup, Cheongju, 28119, Korea (the Republic of)
| | - Ippeita Dan
- Faculty of Science and Engineering, Chuo University, Tama Campus 742-1 Higashinakano Hachioji-shi, Tokyo, 192-0393, JAPAN
| | - Sungho Tak
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, 162 Yeongudanji-ro, Cheongwon-gu, Ochang-eup, Cheongju, 28119, Korea (the Republic of)
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26
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Abstract
Purpose of Review Near infrared spectroscopy (NIRS) is a non-invasive optical technique that uses near infrared light to detect the oxygenation status and hemodynamics of various organs. This article reviews the use of NIRS for the non-invasive assessment of lower urinary tract dysfunction (LUTD). Applications include assessment of bladder outlet obstruction, overactive and underactive bladder, neurogenic LUTD, pediatric LUTD, interstitial cystitis/bladder pain syndrome, and pelvic floor dysfunction. In addition, the article describes how NIRS is elucidating more about the brain-bladder connection. Technological advancements enabling these applications are also discussed. Recent Findings While evidence exists for the application of NIRS throughout a wide range of LUTD, most of these studies are limited by small sample sizes without matched controls. Investigators have experienced problems with reproducibility and motion artifacts contaminating the data. The literature is also becoming dated with use of older technology. Summary NIRS holds potential for the non-invasive acquisition of urodynamic information over time scales and activities not previously accessible, but it is not yet ready for use in routine clinical practice. Advances in wearable technology will address some of the current limitations of NIRS, but to realize its full potential, larger scale validation studies will be required. Moreover, multidisciplinary collaboration between clinicians, scientists, engineers, and patient advocates will be critical to further optimize these systems.
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27
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LIONirs: flexible Matlab toolbox for fNIRS data analysis. J Neurosci Methods 2022; 370:109487. [DOI: 10.1016/j.jneumeth.2022.109487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 11/21/2022]
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28
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Fló A, Gennari G, Benjamin L, Dehaene-Lambertz G. Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental cognitive studies. Dev Cogn Neurosci 2022; 54:101077. [PMID: 35093730 PMCID: PMC8804179 DOI: 10.1016/j.dcn.2022.101077] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 01/01/2023] Open
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29
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Mohammad PPS, Isarangura S, Eddins A, Parthasarathy AB. Comparison of functional activation responses from the auditory cortex derived using multi-distance frequency domain and continuous wave near-infrared spectroscopy. NEUROPHOTONICS 2021; 8:045004. [PMID: 34926716 PMCID: PMC8673635 DOI: 10.1117/1.nph.8.4.045004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 11/29/2021] [Indexed: 05/08/2023]
Abstract
Significance: Quantitative measurements of cerebral hemodynamic changes due to functional activation are widely accomplished with commercial continuous wave (CW-NIRS) instruments despite the availability of the more rigorous multi-distance frequency domain (FD-NIRS) approach. A direct comparison of the two approaches to functional near-infrared spectroscopy can help in the interpretation of optical data and guide implementations of diffuse optical instruments for measuring functional activation. Aim: We explore the differences between CW-NIRS and multi-distance FD-NIRS by comparing measurements of functional activation in the human auditory cortex. Approach: Functional activation of the human auditory cortex was measured using a commercial frequency domain near-infrared spectroscopy instrument for 70 dB sound pressure level broadband noise and pure tone (1000 Hz) stimuli. Changes in tissue oxygenation were calculated using the modified Beer-Lambert law (CW-NIRS approach) and the photon diffusion equation (FD-NIRS approach). Results: Changes in oxygenated hemoglobin measured with the multi-distance FD-NIRS approach were about twice as large as those measured with the CW-NIRS approach. A finite-element simulation of the functional activation problem was performed to demonstrate that tissue oxygenation changes measured with the CW-NIRS approach is more accurate than that with multi-distance FD-NIRS. Conclusions: Multi-distance FD-NIRS approaches tend to overestimate functional activation effects, in part due to partial volume effects.
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Affiliation(s)
| | - Sittiprapa Isarangura
- University of South Florida, Department of Communication Sciences and Disorders, Tampa, Florida, United States
| | - Ann Eddins
- University of South Florida, Department of Communication Sciences and Disorders, Tampa, Florida, United States
| | - Ashwin B. Parthasarathy
- University of South Florida, Department of Electrical Engineering, Tampa, Florida, United States
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30
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Huo C, Xu G, Li W, Xie H, Zhang T, Liu Y, Li Z. A review on functional near-infrared spectroscopy and application in stroke rehabilitation. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2021. [DOI: 10.1016/j.medntd.2021.100064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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31
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Li Z, Li J, Hong B, Nolte G, Engel AK, Zhang D. Speaker-Listener Neural Coupling Reveals an Adaptive Mechanism for Speech Comprehension in a Noisy Environment. Cereb Cortex 2021; 31:4719-4729. [PMID: 33969389 DOI: 10.1093/cercor/bhab118] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/25/2021] [Indexed: 01/01/2023] Open
Abstract
Comprehending speech in noise is an essential cognitive skill for verbal communication. However, it remains unclear how our brain adapts to the noisy environment to achieve comprehension. The present study investigated the neural mechanisms of speech comprehension in noise using an functional near-infrared spectroscopy-based inter-brain approach. A group of speakers was invited to tell real-life stories. The recorded speech audios were added with meaningless white noise at four signal-to-noise levels and then played to listeners. Results showed that speaker-listener neural couplings of listener's left inferior frontal gyri (IFG), that is, sensorimotor system, and right middle temporal gyri (MTG), angular gyri (AG), that is, auditory system, were significantly higher in listening conditions than in the baseline. More importantly, the correlation between neural coupling of listener's left IFG and the comprehension performance gradually became more positive with increasing noise level, indicating an adaptive role of sensorimotor system in noisy speech comprehension; however, the top behavioral correlations for the coupling of listener's right MTG and AG were only obtained in mild noise conditions, indicating a different and less robust mechanism. To sum up, speaker-listener coupling analysis provides added value and new sight to understand the neural mechanism of speech-in-noise comprehension.
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Affiliation(s)
- Zhuoran Li
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China.,Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Jiawei Li
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China.,Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Bo Hong
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China.,Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg Eppendorf, Hamburg 20246, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg Eppendorf, Hamburg 20246, Germany
| | - Dan Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China.,Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
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32
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Richardson H, Taylor J, Kane-Grade F, Powell L, Bosquet Enlow M, Nelson C. Preferential responses to faces in superior temporal and medial prefrontal cortex in three-year-old children. Dev Cogn Neurosci 2021; 50:100984. [PMID: 34246062 PMCID: PMC8274289 DOI: 10.1016/j.dcn.2021.100984] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/04/2021] [Accepted: 06/29/2021] [Indexed: 10/25/2022] Open
Abstract
Perceiving faces and understanding emotions are key components of human social cognition. Prior research with adults and infants suggests that these social cognitive functions are supported by superior temporal cortex (STC) and medial prefrontal cortex (MPFC). We used functional near-infrared spectroscopy (fNIRS) to characterize functional responses in these cortical regions to faces in early childhood. Three-year-old children (n = 88, M(SD) = 3.15(.16) years) passively viewed faces that varied in emotional content and valence (happy, angry, fearful, neutral) and, for fearful and angry faces, intensity (100%, 40%), while undergoing fNIRS. Bilateral STC and MPFC showed greater oxygenated hemoglobin concentration values to all faces relative to objects. MPFC additionally responded preferentially to happy faces relative to neutral faces. We did not detect preferential responses to angry or fearful faces, or overall differences in response magnitude by emotional valence (100% happy vs. fearful and angry) or intensity (100% vs. 40% fearful and angry). In exploratory analyses, preferential responses to faces in MPFC were not robustly correlated with performance on tasks of early social cognition. These results link and extend adult and infant research on functional responses to faces in STC and MPFC and contribute to the characterization of the neural correlates of early social cognition.
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Affiliation(s)
- H. Richardson
- Department of Pediatrics, Boston Children’s Hospital, United States
- Department of Pediatrics, Harvard Medical School, United States
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, United Kingdom
| | - J. Taylor
- Department of Pediatrics, Boston Children’s Hospital, United States
- Department of Pediatrics, Harvard Medical School, United States
| | - F. Kane-Grade
- Department of Pediatrics, Boston Children’s Hospital, United States
- Department of Pediatrics, Harvard Medical School, United States
- Institute of Child Development, University of Minnesota, United States
| | - L. Powell
- Department of Psychology, University of California San Diego, United States
| | - M. Bosquet Enlow
- Department of Psychiatry, Boston Children’s Hospital, United States
- Department of Psychiatry, Harvard Medical School, United States
| | - C.A. Nelson
- Department of Pediatrics, Boston Children’s Hospital, United States
- Department of Pediatrics, Harvard Medical School, United States
- Graduate School of Education, Harvard University, United States
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33
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A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking. SENSORS 2021; 21:s21155117. [PMID: 34372353 PMCID: PMC8346954 DOI: 10.3390/s21155117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/13/2021] [Accepted: 07/26/2021] [Indexed: 12/13/2022]
Abstract
Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes' movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes' movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes' movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.
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34
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fNIRS & e-drum: An ecological approach to monitor hemodynamic and behavioural effects of rhythmic auditory cueing training. Brain Cogn 2021; 151:105753. [PMID: 34020165 DOI: 10.1016/j.bandc.2021.105753] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 04/03/2021] [Accepted: 05/03/2021] [Indexed: 01/05/2023]
Abstract
Converging evidence suggests a beneficial effect of rhythmic music-therapy in easing motor dysfunctions. Nevertheless, the neural systems underpinning both the direct effect and the influence of rhythm on movement control and execution during training in ecological settings are still largely unknown. In this study, we propose an ecological approach to monitor brain activity and behavioural performance during rhythmic auditory cueing short-term training. Our approach envisages the combination of functional near-infrared spectroscopy (fNIRS), which is a non-invasive neuroimaging technique that allows unconstrained movements of participants, with electronic drum (e-drum), which is an instrument able to collect behavioural tapping data in real time. The behavioural and brain effects of this short-term training were investigated on a group of healthy participants, who well tolerated the experimental settings, since none of them withdrew from the study. The rhythmic auditory cueing short-term training improved beat regularity and decreased group variability. At the group level, the training resulted in a reduction of brain activity primarily in premotor areas. Furthermore, participants with the highest behavioural improvement during training showed the smallest reduction in brain activity. Overall, we conclude that our study could pave the way towards translating the proposed approach to clinical settings.
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Dans PW, Foglia SD, Nelson AJ. Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research. Brain Sci 2021; 11:606. [PMID: 34065136 PMCID: PMC8151801 DOI: 10.3390/brainsci11050606] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/05/2021] [Accepted: 05/07/2021] [Indexed: 12/26/2022] Open
Abstract
FNIRS pre-processing and processing methodologies are very important-how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.
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Affiliation(s)
- Patrick W. Dans
- Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada;
| | - Stevie D. Foglia
- School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada;
| | - Aimee J. Nelson
- Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada;
- School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada;
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Slutter MWJ, Thammasan N, Poel M. Exploring the Brain Activity Related to Missing Penalty Kicks: An fNIRS Study. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.661466] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
At vital moments in professional soccer matches, penalties were often missed. Psychological factors, such as anxiety and pressure, are among the critical causes of the mistakes, commonly known as choking under pressure. Nevertheless, the factors have not been fully explored. In this study, we used functional near-infrared spectroscopy (fNIRS) to investigate the influence of the brain on this process. An in-situ study was set-up (N = 22), in which each participant took 15 penalties under three different pressure conditions: without a goalkeeper, with an amiable goalkeeper, and with a competitive goalkeeper. Both experienced and inexperienced soccer players were recruited, and the brain activation was compared across groups. Besides, fNIRS activation was compared between sessions that participants felt anxious against sessions without anxiety report, and between penalty-scoring and -missing sessions. The results show that the task-relevant brain region, the motor cortex, was more activated when players were not experiencing performance anxiety. The activation of task-irrelevant areas was shown to be related to players experiencing anxiety and missing penalties, especially the prefrontal cortex (PFC). More particularly, an overall higher activation of the PFC and an increase of PFC lateral asymmetry were related to anxious players and missed penalties, which can be caused by players' worries about the consequences of scoring or missing the penalty kicks. When experienced players were feeling anxious, their left temporal cortex activation increased, which could be an indication that experienced overthink the situation and neglect their automated skills. Besides, the left temporal cortex activation is higher when inexperienced players succeeded to score a penalty. Overall, the results of this study are in line with the neural efficiency theory and demonstrate the feasibility and ecological validity to detect neurological clues relevant to anxiety and performance from fNIRS recordings in the field.
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Alexopoulos J, Giordano V, Janda C, Benavides-Varela S, Seidl R, Doering S, Berger A, Bartha-Doering L. The duration of intrauterine development influences discrimination of speech prosody in infants. Dev Sci 2021; 24:e13110. [PMID: 33817911 DOI: 10.1111/desc.13110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/17/2021] [Accepted: 03/05/2021] [Indexed: 11/26/2022]
Abstract
Auditory speech discrimination is essential for normal language development. Children born preterm are at greater risk of language developmental delays. Using functional near-infrared spectroscopy at term-equivalent age, the present study investigated early discrimination of speech prosody in 62 neonates born between week 23 and 41 of gestational age (GA). We found a significant positive correlation between GA at birth and neural discrimination of forward versus backward speech at term-equivalent age. Cluster analysis identified a critical threshold at around week 32 of GA, pointing out the existence of subgroups. Infants born before week 32 of GA exhibited a significantly different pattern of hemodynamic response to speech stimuli compared to infants born at or after week 32 of GA. Thus, children born before the GA of 32 weeks are especially vulnerable to early speech discrimination deficits. To support their early language development, we therefore suggest a close follow-up and additional speech and language therapy especially in the group of children born before week 32 of GA.
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Affiliation(s)
- Johanna Alexopoulos
- Department of Psychoanalysis and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Vito Giordano
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Charlotte Janda
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Silvia Benavides-Varela
- Department of Developmental Psychology and Socialization, University of Padova, Padova, Italy
| | - Rainer Seidl
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Stephan Doering
- Department of Psychoanalysis and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Angelika Berger
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Lisa Bartha-Doering
- Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
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Forbes SH, Wijeakumar S, Eggebrecht AT, Magnotta VA, Spencer JP. Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy. NEUROPHOTONICS 2021; 8:025010. [PMID: 35106319 PMCID: PMC8786393 DOI: 10.1117/1.nph.8.2.025010] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/18/2021] [Indexed: 05/29/2023]
Abstract
Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy. Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.
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Affiliation(s)
- Samuel H. Forbes
- University of East Anglia, School of Psychology, Lawrence Stenhouse Building, Norwich, United Kingdom
| | | | - Adam T. Eggebrecht
- Washington University, Mallinckrodt Institute of Radiology, St Louis, Missouri, United States
| | | | - John P. Spencer
- University of East Anglia, School of Psychology, Lawrence Stenhouse Building, Norwich, United Kingdom
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Gemignani J, Gervain J. Comparing different pre-processing routines for infant fNIRS data. Dev Cogn Neurosci 2021; 48:100943. [PMID: 33735718 PMCID: PMC7985709 DOI: 10.1016/j.dcn.2021.100943] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 02/25/2021] [Accepted: 03/09/2021] [Indexed: 01/24/2023] Open
Abstract
Five pre-processing pipelines were compared on synthetic and real infant NIRS data. Strict inclusion criteria limit the number of trials, but recover HRF accurately. Artifact correction retains larger amounts of data, but may lower HRF amplitude. No difference between applying pre-processing to optical density or concentration data.
Functional Near Infrared Spectroscopy (fNIRS) is an important neuroimaging technique in cognitive developmental neuroscience. Nevertheless, there is no general consensus yet about best pre-processing practices. This issue is highly relevant, especially since the development and variability of the infant hemodynamic response (HRF) is not fully known. Systematic comparisons between analysis methods are thus necessary. We investigated the performance of five different pipelines, selected on the basis of a systematic search of the infant NIRS literature, in two experiments. In Experiment 1, we used synthetic data to compare the recovered HRFs with the true HRF and to assess the robustness of each method against increasing levels of noise. In Experiment 2, we analyzed experimental data from a published study, which assessed the neural correlates of artificial grammar processing in newborns. We found that with motion artifact correction (as opposed to rejection) a larger number of trials were retained, but HRF amplitude was often strongly reduced. By contrast, artifact rejection resulted in a high exclusion rate but preserved adequately the characteristics of the HRF. We also found that the performance of all pipelines declined as the noise increased, but significantly less so than if no pre-processing was applied. Finally, we found no difference between running the pre-processing on optical density or concentration change data. These results suggest that pre-processing should thus be optimized as a function of the specific quality issues a give dataset exhibits.
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Affiliation(s)
- Jessica Gemignani
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy; Integrative Neuroscience and Cognition Center, CNRS & University of Paris, Paris, France.
| | - Judit Gervain
- Department of Developmental Psychology and Socialisation, University of Padova, Padova, Italy; Integrative Neuroscience and Cognition Center, CNRS & University of Paris, Paris, France
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40
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Neonatal NIRS monitoring: recommendations for data capture and review of analytics. J Perinatol 2021; 41:675-688. [PMID: 33589724 PMCID: PMC7883881 DOI: 10.1038/s41372-021-00946-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/20/2020] [Accepted: 01/19/2021] [Indexed: 01/29/2023]
Abstract
Brain injury is one of the most consequential problems facing neonates, with many preterm and term infants at risk for cerebral hypoxia and ischemia. To develop effective neuroprotective strategies, the mechanistic basis for brain injury must be understood. The fragile state of neonates presents unique research challenges; invasive measures of cerebral blood flow and oxygenation assessment exceed tolerable risk profiles. Near-infrared spectroscopy (NIRS) can safely and non-invasively estimate cerebral oxygenation, a correlate of cerebral perfusion, offering insight into brain injury-related mechanisms. Unfortunately, lack of standardization in device application, recording methods, and error/artifact correction have left the field fractured. In this article, we provide a framework for neonatal NIRS research. Our goal is to provide a rational basis for NIRS data capture and processing that may result in better comparability between studies. It is also intended to serve as a primer for new NIRS researchers and assist with investigation initiation.
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41
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Butler LK, Kiran S, Tager-Flusberg H. Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2020; 29:1674-1701. [PMID: 32640168 PMCID: PMC7893520 DOI: 10.1044/2020_ajslp-19-00050] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Purpose Functional brain imaging is playing an increasingly important role in the diagnosis and treatment of communication disorders, yet many populations and settings are incompatible with functional magnetic resonance imaging and other commonly used techniques. We conducted a systematic review of neuroimaging studies using functional near-infrared spectroscopy (fNIRS) with individuals with speech or language impairment across the life span. We aimed to answer the following question: To what extent has fNIRS been used to investigate the neural correlates of speech-language impairment? Method This systematic review was preregistered with PROSPERO, the international prospective register of systematic reviews (CRD42019136464). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for preferred reporting items for systematic reviews. The database searches were conducted between February and March of 2019 with the following search terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Results We found 34 fNIRS studies that involved individuals with speech or language impairment across nine categories: (a) autism spectrum disorders; (b) developmental speech and language disorders; (c) cochlear implantation and deafness; (d) dementia, dementia of the Alzheimer's type, and mild cognitive impairment; (e) locked-in syndrome; (f) neurologic speech disorders/dysarthria; (g) stroke/aphasia; (h) stuttering; and (i) traumatic brain injury. Conclusions Though it is not without inherent challenges, fNIRS may have advantages over other neuroimaging techniques in the areas of speech and language impairment. fNIRS has clinical applications that may lead to improved early and differential diagnosis, increase our understanding of response to treatment, improve neuroprosthetic functioning, and advance neurofeedback.
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Affiliation(s)
- Lindsay K. Butler
- Sargent College of Health and Rehabilitation Sciences, Boston University, MA
| | - Swathi Kiran
- Sargent College of Health and Rehabilitation Sciences, Boston University, MA
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42
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Sherafati A, Snyder AZ, Eggebrecht AT, Bergonzi KM, Burns-Yocum TM, Lugar HM, Ferradal SL, Robichaux-Viehoever A, Smyser CD, Palanca BJ, Hershey T, Culver JP. Global motion detection and censoring in high-density diffuse optical tomography. Hum Brain Mapp 2020; 41:4093-4112. [PMID: 32648643 PMCID: PMC8022277 DOI: 10.1002/hbm.25111] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 06/05/2020] [Accepted: 06/10/2020] [Indexed: 12/30/2022] Open
Abstract
Motion‐induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high‐density diffuse optical tomography (HD‐DOT) with hundreds to thousands of source‐detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near‐infrared spectroscopy (fNIRS). This limitation restricts the application of HD‐DOT in many challenging imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion—with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD‐based motion censoring on both hearing words task and resting state HD‐DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation‐based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring on HD‐DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.
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Affiliation(s)
- Arefeh Sherafati
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University School of Medicine in St, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Adam T Eggebrecht
- Department of Radiology, Washington University School of Medicine in St, St. Louis, Missouri, USA.,Department of Biomedical Engineering, Washington University School in St. Louis, St. Louis, Missouri, USA.,Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | | | - Tracy M Burns-Yocum
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - Heather M Lugar
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Silvina L Ferradal
- Department Of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, USA
| | | | - Christopher D Smyser
- Department of Radiology, Washington University School of Medicine in St, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ben J Palanca
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Tamara Hershey
- Department of Radiology, Washington University School of Medicine in St, St. Louis, Missouri, USA.,Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - Joseph P Culver
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Radiology, Washington University School of Medicine in St, St. Louis, Missouri, USA.,Department of Biomedical Engineering, Washington University School in St. Louis, St. Louis, Missouri, USA.,Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Delgado Reyes L, Wijeakumar S, Magnotta VA, Forbes SH, Spencer JP. The functional brain networks that underlie visual working memory in the first two years of life. Neuroimage 2020; 219:116971. [PMID: 32454208 PMCID: PMC7443700 DOI: 10.1016/j.neuroimage.2020.116971] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 05/14/2020] [Accepted: 05/16/2020] [Indexed: 01/23/2023] Open
Abstract
Visual working memory (VWM) is a central cognitive system used to compare views of the world and detect changes in the local environment. This system undergoes dramatic development in the first two years; however, we know relatively little about the functional organization of VWM at the level of the brain. Here, we used image-based functional near-infrared spectroscopy (fNIRS) to test four hypotheses about the spatial organization of the VWM network in early development. Four-month-olds, 1-year-olds, and 2-year-olds completed a VWM task while we recorded neural activity from 19 cortical regions-of-interest identified from a meta-analysis of the adult fMRI literature on VWM. Results showed significant task-specific functional activation near 6 of 19 ROIs, revealing spatial consistency in the brain regions activated in our study and brain regions identified to be part of the VWM network in adult fMRI studies. Working memory related activation was centered on bilateral anterior intraparietal sulcus (aIPS), left temporoparietal junction (TPJ), and left ventral occipital complex (VOC), while visual exploratory measures were associated with activation in right dorsolateral prefrontal cortex, left TPJ, and bilateral IPS. Results show that a distributed brain network underlies functional changes in VWM in infancy, revealing new insights into the neural mechanisms that support infants’ improved ability to remember visual information and to detect changes in an on-going visual stream. A distributed brain network underlies functional changes in VWM in infancy and toddlerhood. This network shows robust engagement of similar brain regions identified in fMRI studies with adults as early as four months. Working memory related activation was centered on bilateral anterior intraparietal sulcus, left temporoparietal junction, and left ventral occipital complex Visual exploratory measures were associated with activation in right dorsolateral prefrontal cortex, bilateral anterior intraparietal sulcus, and left temporoparietal junction.
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Affiliation(s)
- Lourdes Delgado Reyes
- School of Psychology, University of East Anglia, UK; Department of Psychology, University of Pennsylvania, USA
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von Lühmann A, Ortega-Martinez A, Boas DA, Yücel MA. Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Front Hum Neurosci 2020; 14:30. [PMID: 32132909 PMCID: PMC7040364 DOI: 10.3389/fnhum.2020.00030] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 01/22/2020] [Indexed: 11/28/2022] Open
Abstract
Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.
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Affiliation(s)
- Alexander von Lühmann
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States.,Machine Learning Department, Berlin Institute of Technology, Berlin, Germany
| | | | - David A Boas
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
| | - Meryem Ayşe Yücel
- Neurophotonics Center, Biomedical Engineering, Boston University, Boston, MA, United States
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Di Lorenzo R, Pirazzoli L, Blasi A, Bulgarelli C, Hakuno Y, Minagawa Y, Brigadoi S. Recommendations for motion correction of infant fNIRS data applicable to multiple data sets and acquisition systems. Neuroimage 2019; 200:511-527. [DOI: 10.1016/j.neuroimage.2019.06.056] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 06/11/2019] [Accepted: 06/24/2019] [Indexed: 10/26/2022] Open
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Putt SS, Wijeakumar S, Spencer JP. Prefrontal cortex activation supports the emergence of early stone age toolmaking skill. Neuroimage 2019; 199:57-69. [DOI: 10.1016/j.neuroimage.2019.05.056] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/20/2019] [Accepted: 05/21/2019] [Indexed: 01/01/2023] Open
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Wijeakumar S, Kumar A, Delgado Reyes LM, Tiwari M, Spencer JP. Early adversity in rural India impacts the brain networks underlying visual working memory. Dev Sci 2019; 22:e12822. [PMID: 30803122 PMCID: PMC6767418 DOI: 10.1111/desc.12822] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 12/26/2018] [Accepted: 02/12/2019] [Indexed: 12/16/2022]
Abstract
There is a growing need to understand the global impact of poverty on early brain and behavioural development, particularly with regard to key cognitive processes that emerge in early development. Although the impact of adversity on brain development can trap children in an intergenerational cycle of poverty, the massive potential for brain plasticity is also a source of hope: reliable, accessible, culturally agnostic methods to assess early brain development in low resource settings might be used to measure the impact of early adversity, identify infants for timely intervention and guide the development and monitor the effectiveness of early interventions. Visual working memory (VWM) is an early marker of cognitive capacity that has been assessed reliably in early infancy and is predictive of later academic achievement in Western countries. Here, we localized the functional brain networks that underlie VWM in early development in rural India using a portable neuroimaging system, and we assessed the impact of adversity on these brain networks. We recorded functional brain activity as young children aged 4-48 months performed a VWM task. Brain imaging results revealed localized activation in the frontal cortex, replicating findings from a Midwestern US sample. Critically, children from families with low maternal education and income showed weaker brain activity and poorer distractor suppression in canonical working memory areas in the left frontal cortex. Implications of this work are far-reaching: it is now cost-effective to localize functional brain networks in early development in low-resource settings, paving the way for novel intervention and assessment methods.
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Affiliation(s)
| | - Aarti Kumar
- Community Empowerment LabUttar PradeshLucknowIndia
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48
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Jackson ES, Wijeakumar S, Beal DS, Brown B, Zebrowski P, Spencer JP. A fNIRS Investigation of Speech Planning and Execution in Adults Who Stutter. Neuroscience 2019; 406:73-85. [DOI: 10.1016/j.neuroscience.2019.02.032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 02/25/2019] [Accepted: 02/26/2019] [Indexed: 01/05/2023]
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49
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Schytz HW, Amin FM, Selb J, Boas DA. Non-invasive methods for measuring vascular changes in neurovascular headaches. J Cereb Blood Flow Metab 2019; 39:633-649. [PMID: 28782410 PMCID: PMC6446419 DOI: 10.1177/0271678x17724138] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Vascular changes during spontaneous headache attacks have been studied over the last 30 years. The interest in cerebral vessels in headache research was initially due to the hypothesis of cerebral vessels as the pain source. Here, we review the knowledge gained by measuring the cerebral vasculature during spontaneous primary headache attacks with the use of single photon emission tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRA) and transcranial Doppler (TCD). Furthermore, the use of near-infrared spectroscopy in headache research is reviewed. Existing TCD studies of migraine and other headache disorders do not provide solid evidence for cerebral blood flow velocity changes during spontaneous attacks of migraine headache. SPECT studies have clearly shown cortical vascular changes following migraine aura and the differences between migraine with aura compared to migraine without aura. PET studies have shown focal activation in brain structures related to headache, but whether the changes are specific to different primary headaches have yet to be demonstrated. MR angiography has shown precise changes in large cerebral vessels during spontaneous migraine without aura attacks. Future development in more precise imaging methods may further elucidate the pathophysiological mechanisms in primary headaches.
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Affiliation(s)
- Henrik W Schytz
- 1 Danish Headache Center and Department of Neurology, Rigshospitalet Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Faisal M Amin
- 1 Danish Headache Center and Department of Neurology, Rigshospitalet Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Juliette Selb
- 2 Department of Radiology, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - David A Boas
- 2 Department of Radiology, MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
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Pinti P, Scholkmann F, Hamilton A, Burgess P, Tachtsidis I. Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework. Front Hum Neurosci 2019; 12:505. [PMID: 30687038 PMCID: PMC6336925 DOI: 10.3389/fnhum.2018.00505] [Citation(s) in RCA: 180] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 12/03/2018] [Indexed: 11/24/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.
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Affiliation(s)
- Paola Pinti
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Felix Scholkmann
- Department of Neonatology, Biomedical Optics Research Laboratory, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Antonia Hamilton
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Paul Burgess
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Ilias Tachtsidis
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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