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Su WC, Colacot R, Ahmed N, Nguyen T, George T, Gandjbakhche A. The use of functional near-infrared spectroscopy in tracking neurodevelopmental trajectories in infants and children with or without developmental disorders: a systematic review. Front Psychiatry 2023; 14:1210000. [PMID: 37779610 PMCID: PMC10536152 DOI: 10.3389/fpsyt.2023.1210000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023] Open
Abstract
Understanding the neurodevelopmental trajectories of infants and children is essential for the early identification of neurodevelopmental disorders, elucidating the neural mechanisms underlying the disorders, and predicting developmental outcomes. Functional Near-Infrared Spectroscopy (fNIRS) is an infant-friendly neuroimaging tool that enables the monitoring of cerebral hemodynamic responses from the neonatal period. Due to its advantages, fNIRS is a promising tool for studying neurodevelopmental trajectories. Although many researchers have used fNIRS to study neural development in infants/children and have reported important findings, there is a lack of synthesized evidence for using fNIRS to track neurodevelopmental trajectories in infants and children. The current systematic review summarized 84 original fNIRS studies and showed a general trend of age-related increase in network integration and segregation, interhemispheric connectivity, leftward asymmetry, and differences in phase oscillation during resting-state. Moreover, typically developing infants and children showed a developmental trend of more localized and differentiated activation when processing visual, auditory, and tactile information, suggesting more mature and specialized sensory networks. Later in life, children switched from recruiting bilateral auditory to a left-lateralized language circuit when processing social auditory and language information and showed increased prefrontal activation during executive functioning tasks. The developmental trajectories are different in children with developmental disorders, with infants at risk for autism spectrum disorder showing initial overconnectivity followed by underconnectivity during resting-state; and children with attention-deficit/hyperactivity disorders showing lower prefrontal cortex activation during executive functioning tasks compared to their typically developing peers throughout childhood. The current systematic review supports the use of fNIRS in tracking the neurodevelopmental trajectories in children. More longitudinal studies are needed to validate the neurodevelopmental trajectories and explore the use of these neurobiomarkers for the early identification of developmental disorders and in tracking the effects of interventions.
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Affiliation(s)
| | | | | | | | | | - Amir Gandjbakhche
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Bethesda, MD, United States
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Ma Y, Luo K, Ding P, Yin S, Li X, Li Y. Differences in symmetrical low-frequency oscillations among healthy subjects, and those with stroke or peripheral arterial disease. Heliyon 2023; 9:e17015. [PMID: 37484434 PMCID: PMC10361110 DOI: 10.1016/j.heliyon.2023.e17015] [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: 03/06/2023] [Revised: 05/30/2023] [Accepted: 06/04/2023] [Indexed: 07/25/2023] Open
Abstract
Low-frequency oscillations (LFOs) observed in near-infrared spectroscopy (NIRS) reflect autonomic physiological processes, and may serve as useful indicators for detecting and monitoring circulatory dysfunction. The aim of this study was to reveal whether LFOs can be used as vascular perfusion biomarkers to differentiate different types and degrees of vascular lesions based on clinical patient data. Materials and Methods: In this study, healthy controls, ischemic stroke patients and peripheral atherosclerosis patients completed a resting-state LFO detection experiment. LFOs were collected simultaneously at peripheral right and left earlobes, fingertips and toes, along with coherence and phase shift analyses processing. Results: The results showed that the coherence coefficients of symmetric peripheral positions and the absolute value-phase shifts of fingers and toes can be used to distinguish healthy individuals, ischemic stroke patients and peripheral atherosclerosis patients. The symmetric earlobes' absolute value-phase shifts could be used to differentiate mild and severe ischemic stroke patients; the coherence coefficients and absolute value-phase shifts of the symmetric toes could be used to differentiate mild and severe peripheral arteriosclerosis patients. The accuracy of differentiating between types of patients was 70%; those with different degrees of peripheral atherosclerosis was 85%, and those with different degrees of ischemic stroke was 72%. Conclusions: LFOs can serve as vascular perfusion biomarkers to differentiate types and degrees of vascular lesions. Therefore, LFOs have the potential to provide valuable patient information to assist researchers and clinicians in identifying specific peripheral circulatory damage subgroups.
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Affiliation(s)
- Yunfei Ma
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
| | - Kexin Luo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
| | - Peng Ding
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
| | - Shimin Yin
- Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Xiaoli Li
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China
| | - Yingwei Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
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Gervain J, Minagawa Y, Emberson L, Lloyd-Fox S. Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges. NEUROPHOTONICS 2023; 10:023519. [PMID: 37020727 PMCID: PMC10068680 DOI: 10.1117/1.nph.10.2.023519] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
Significance Functional near-infrared spectroscopy (fNIRS) is a frequently used neuroimaging tool to explore the developing brain, particularly in infancy, with studies spanning from birth to toddlerhood (0 to 2 years). We provide an overview of the challenges and opportunities that the developmental fNIRS field faces, after almost 25 years of research. Aim We discuss the most recent advances in fNIRS brain imaging with infants and outlines the trends and perspectives that will likely influence progress in the field in the near future. Approach We discuss recent progress and future challenges in various areas and applications of developmental fNIRS from methodological and technological innovations to data processing and statistical approaches. Results and Conclusions The major trends identified include uses of fNIRS "in the wild," such as global health contexts, home and community testing, and hyperscanning; advances in hardware, such as wearable technology; assessment of individual variation and developmental trajectories particularly while embedded in studies examining other environmental, health, and context specific factors and longitudinal designs; statistical advances including resting-state network and connectivity, machine learning and reproducibility, and collaborative studies. Standardization and larger studies have been, and will likely continue to be, a major goal in the field, and new data analysis techniques, statistical methods, and collaborative cross-site projects are emerging.
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Affiliation(s)
- Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- University of Padua, Padova Neuroscience Center, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Yasuyo Minagawa
- Keio University, Department of Psychology, Faculty of Letters, Yokohama, Japan
| | - Lauren Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Sarah Lloyd-Fox
- University of Cambridge, Department of Psychology, Cambridge, United Kingdom
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Gao T, Zou C, Li J, Han C, Zhang H, Li Y, Tang X, Fan Y. Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods. JOURNAL OF BIOPHOTONICS 2022; 15:e202100388. [PMID: 35102703 DOI: 10.1002/jbio.202100388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/28/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated hemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. For 20-min signals, the 10-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-min signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. The method proposed in this article has potential for detecting and screening moyamoya with high proficiency. Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases.
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Affiliation(s)
- Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Chuyue Zou
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jinyu Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Cong Han
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
- Medical School of Chinese PLA, Beijing, China
| | - Houdi Zhang
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Yue Li
- School of Medicine, Tsinghua University, Beijing, China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
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5
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Liang Z, Wang Y, Tian H, Gu Y, Arimitsu T, Takahashi T, Minagawa Y, Niu H, Tong Y. Spatial complexity method for tracking brain development and degeneration using functional near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:1718-1736. [PMID: 35414994 PMCID: PMC8973163 DOI: 10.1364/boe.449341] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/07/2022] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (Δ[HbO]) or two deoxy-hemoglobin (Δ[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE's performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDEHbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yuxi Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Hao Tian
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yue Gu
- Key Laboratory of Computer Vision and System (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Takeshi Arimitsu
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Takao Takahashi
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Yasuyo Minagawa
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Haijing Niu
- Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Basic Examination of Haemoglobin Phase of Oxygenation and Deoxygenation in Resting State and Task Periods in Adults Using fNIRS. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1395:189-198. [PMID: 36527636 DOI: 10.1007/978-3-031-14190-4_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used to measure the relative changes in concentrations of oxygenated haemoglobin (oxy-Hb) and deoxygenated haemoglobin (deoxy-Hb) in the cerebral cortex. While most previous studies using fNIRS have relied only on a single oxy-Hb or deoxy-Hb parameter to infer about neural activation, the phase difference between the oxy- and deoxy-Hb signals (haemoglobin phase of oxygenation and deoxygenation: hPod) has been reported to be an important biomarker for analysing haemodynamic characteristics of the brain in infants. In this study, we examined the basic characteristics of adult hPod to develop a new analysis method to detect more sensitive signals that reflect neural activation in adults using fNIRS. We measured the hPod of 12 healthy adults in the frontal and occipital cortex during rest and upon exposure to visual stimuli and the verbal working memory (WM) task. We found that the average hPod values during the entire measurement period ranged between π and 1.5π rad in all conditions. This result indicates that the phase differences in adults were generally close to a stable antiphase pattern (hPod values around π), regardless of the presence or absence of tasks and stimuli. However, when dynamic changes in hPod values were analysed, significant differences between the resting state and WM tasks were observed during activation period in the frontal and occipital regions. These results suggest that the analysis of dynamic hPod change is useful for detecting a subtle activation for cognitive tasks.
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Liang Z, Tian H, Yang HC, Arimitsu T, Takahashi T, Sassaroli A, Fantini S, Niu H, Minagawa Y, Tong Y. Tracking Brain Development From Neonates to the Elderly by Hemoglobin Phase Measurement Using Functional Near-Infrared Spectroscopy. IEEE J Biomed Health Inform 2021; 25:2497-2509. [PMID: 33493123 DOI: 10.1109/jbhi.2021.3053900] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The biological and neurological processes during the lifespan are dynamic with significant alterations associated with different stages of life. The phase and coupling of oxy-hemoglobin (Δ[HbO]) and deoxy-hemoglobin concentration changes (Δ[Hb]) measured by functional near-infrared spectroscopy (fNIRS) are shown to characterize the neurovascular and metabolic development of infants. However, the changes in phase and coupling across the human lifespan remain mostly unknown. Here, fNIRS measurements of Δ[HbO] and Δ[Hb] conducted at two sites on different age populations (from newborns to elderly) were combined. Firstly, we assessed the influence of random noise on the calculation of the phase difference and phase-locking index (PLI) in fNIRS measurement. The results showed that the phase difference is close to π as the noise intensity approaches -8 dB, and the coupling strength (i.e., PLI) presents a u-shape curve as the noise increase. Secondly, phase difference and PLI in the frequency range 0.01-0.10 Hz were calculated after denoising. It showed that the phase difference increases from newborns to 3-4-month-olds babies. This phase difference persists throughout adulthood until finally being disrupted in the old age. The children's PLI is the highest, followed by that of adults. These two groups' PLI are significantly higher than those of infants and the elderly (p < 0.001). Lastly, a hemodynamic model was used to explain the observations and found close associations with cerebral autoregulation and speed of blood flow. These results demonstrate that the phase-related parameters measured by fNIRS can be used to study the brain and assess brain health throughout the lifespan.
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Vijayakrishnan Nair V, Kish BR, Yang HCS, Yu Z, Guo H, Tong Y, Liang Z. Monitoring anesthesia using simultaneous functional Near Infrared Spectroscopy and Electroencephalography. Clin Neurophysiol 2021; 132:1636-1646. [PMID: 34034088 DOI: 10.1016/j.clinph.2021.03.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/02/2021] [Accepted: 03/28/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE This study aims to understand the neural and hemodynamic responses during general anesthesia in order to develop a comprehensive multimodal anesthesia depth monitor using simultaneous functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG). METHODS 37 adults and 17 children were monitored with simultaneous fNIRS and EEG, during the complete general anesthesia process. The coupling of fNIRS signals with neuronal signals (EEG) was calculated. Measures of complexity (sample entropy) and phase difference were also quantified from fNIRS signals to identify unique fNIRS based biomarkers of general anesthesia. RESULTS A significant decrease in the complexity and power of fNIRS signals characterize the anesthesia maintenance phase. Furthermore, responses to anesthesia vary between adults and children in terms of neurovascular coupling and frontal EEG alpha power. CONCLUSIONS This study shows that fNIRS signals could reliably quantify the underlying neuronal activity under general anesthesia and clearly distinguish the different phases throughout the procedure in adults and children (with less accuracy). SIGNIFICANCE A multimodal approach incorporating the specific differences between age groups, provides a reliable measure of anesthesia depth.
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Affiliation(s)
| | - Brianna R Kish
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Ho-Ching Shawn Yang
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Zhenyang Yu
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
| | - Hang Guo
- Department of Anesthesiology, the Seventh Medical Center to Chinese PLA General Hospital, Beijing 100700, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.
| | - Zhenhu Liang
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
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Takagi S, Sakuma S, Morita I, Sugimoto E, Yamaguchi Y, Higuchi N, Inamoto K, Ariji Y, Ariji E, Murakami H. Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment. J Clin Med 2020; 9:E3475. [PMID: 33126595 PMCID: PMC7693464 DOI: 10.3390/jcm9113475] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/14/2020] [Accepted: 10/27/2020] [Indexed: 12/05/2022] Open
Abstract
In fields using functional near-infrared spectroscopy (fNIRS), there is a need for an easy-to-understand method that allows visual presentation and rapid analysis of data and test results. This preliminary study examined whether deep learning (DL) could be applied to the analysis of fNIRS-derived brain activity data. To create a visual presentation of the data, an imaging program was developed for the analysis of hemoglobin (Hb) data from the prefrontal cortex in healthy volunteers, obtained by fNIRS before and after tooth clenching. Three types of imaging data were prepared: oxygenated hemoglobin (oxy-Hb) data, deoxygenated hemoglobin (deoxy-Hb) data, and mixed data (using both oxy-Hb and deoxy-Hb data). To differentiate between rest and tooth clenching, a cross-validation test using the image data for DL and a convolutional neural network was performed. The network identification rate using Hb imaging data was relatively high (80‒90%). These results demonstrated that a method using DL for the assessment of fNIRS imaging data may provide a useful analysis system.
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Affiliation(s)
- Shinya Takagi
- Department of Fixed Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan;
| | - Shigemitsu Sakuma
- Department of Fixed Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan;
| | - Ichizo Morita
- Japanese Red Cross Toyota College of Nursing, Toyota 471-8565, Japan;
| | - Eri Sugimoto
- Department of Pediatric Dentistry, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan;
| | - Yoshihiro Yamaguchi
- Department of Fixed Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan;
| | - Naoya Higuchi
- Department of Endodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan; (N.H.); (K.I.)
| | - Kyoko Inamoto
- Department of Endodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan; (N.H.); (K.I.)
| | - Yoshiko Ariji
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan; (Y.A.); (E.A.)
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan; (Y.A.); (E.A.)
| | - Hiroshi Murakami
- Department of Gerodontology and Home Care Dentistry, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan;
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Katus L, Hayes NJ, Mason L, Blasi A, McCann S, Darboe MK, de Haan M, Moore SE, Lloyd-Fox S, Elwell CE. Implementing neuroimaging and eye tracking methods to assess neurocognitive development of young infants in low- and middle-income countries. Gates Open Res 2019; 3:1113. [PMID: 31508580 PMCID: PMC6719506 DOI: 10.12688/gatesopenres.12951.2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2019] [Indexed: 12/12/2022] Open
Abstract
Infants and children in low- and middle-income countries (LMICs) are frequently exposed to a range of environmental risk factors which may negatively affect their neurocognitive development. The mechanisms by which factors such as undernutrition and poverty impact development and cognitive outcomes in early childhood are poorly understood. This lack of knowledge is due in part to a paucity of objective assessment tools which can be implemented across different cultural settings and in very young infants. Over the last decade, technological advances, particularly in neuroimaging, have opened new avenues for research into the developing human brain, allowing us to investigate novel biological associations. This paper presents functional near-infrared spectroscopy (fNIRS), electroencephalography (EEG) and eye tracking (ET) as objective, cross-cultural methods for studying infant neurocognitive development in LMICs, and specifically their implementation in rural Gambia, West Africa. These measures are currently included, as part of a broader battery of assessments, in the Brain Imaging for Global Health (BRIGHT) project, which is developing brain function for age curves in Gambian and UK infants from birth to 24 months of age. The BRIGHT project combines fNIRS, EEG and ET with behavioural, growth, health and sociodemographic measures. The implementation of these measures in rural Gambia are discussed, including methodological and technical challenges that needed to be addressed to ensure successful data acquisition. The aim is to provide guidance to other groups seeking to implement similar methods in their research in other LMICs to better understand associations between environmental risk and early neurocognitive development.
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Affiliation(s)
- Laura Katus
- Great Ormond Street Institute of Child Health, University College London, London, London, WC1N 1EH, UK
| | - Nathan J. Hayes
- GKT School of Medical Education, King’s College London, London, UK
| | - Luke Mason
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
| | - Anna Blasi
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Samantha McCann
- Department of Women and Children’s Health, King's College London, London, UK
| | - Momodou K. Darboe
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia, The Gambia
| | - Michelle de Haan
- Great Ormond Street Institute of Child Health, University College London, London, London, WC1N 1EH, UK
- Great Ormond Street Hospital for Children, London, UK
| | - Sophie E. Moore
- Department of Women and Children’s Health, King's College London, London, UK
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, The Gambia, The Gambia
| | - Sarah Lloyd-Fox
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Clare E. Elwell
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
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11
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Katus L, Hayes NJ, McCann S, Mason L, Blasi A, Darboe MK, de Haan M, Moore SE, Lloyd-Fox S, Elwell CE. Implementing neuroimaging and eye tracking methods to assess neurocognitive development of young infants in low- and middle-income countries. Gates Open Res 2019. [DOI: 10.12688/gatesopenres.12951.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Infants and children in low- and middle-income countries (LMICs) are frequently exposed to a range of environmental risk factors which may negatively affect their neurocognitive development. The mechanisms by which factors such as undernutrition and poverty impact development and cognitive outcomes in early childhood are poorly understood. This lack of knowledge is due in part to a paucity of objective assessment tools which can be implemented across different cultural settings and in very young infants. Over the last decade, technological advances, particularly in neuroimaging, have opened new avenues for research into the developing human brain, allowing us to investigate novel biological associations. This paper presents functional near-infrared spectroscopy (fNIRS), electroencephalography (EEG) and eye tracking (ET) as objective, cross-cultural methods for studying infant neurocognitive development in LMICs, and specifically their implementation in rural Gambia, West Africa. These measures are currently included, as part of a broader battery of assessments, in the Brain Imaging for Global Health (BRIGHT) project, which is developing brain function for age curves in Gambian and UK infants from birth to 24 months of age. The BRIGHT project combines fNIRS, EEG and ET with behavioural, growth, health and sociodemographic measures. The implementation of these measures in rural Gambia are discussed, including methodological and technical challenges that needed to be addressed to ensure successful data acquisition. The aim is to provide guidance to other groups seeking to implement similar methods in their research in other LMICs to better understand associations between environmental risk and early neurocognitive development.
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12
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Liang Z, Minagawa Y, Yang HC, Tian H, Cheng L, Arimitsu T, Takahashi T, Tong Y. Symbolic time series analysis of fNIRS signals in brain development assessment. J Neural Eng 2018; 15:066013. [PMID: 30207540 DOI: 10.1088/1741-2552/aae0c9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Assessing an infant's brain development remains a challenge for neuroscientists and pediatricians despite great technological advances. As a non-invasive neuroimaging tool, functional near-infrared spectroscopy (fNIRS) has great advantages in monitoring an infant's brain activity. To explore the dynamic features of hemodynamic changes in infants, in-pattern exponent (IPE), anti-pattern exponent (APE), as well as permutation cross-mutual information (PCMI) based on symbolic dynamics are proposed to measure the phase differences and coupling strength in oxyhemoglobin (HbO) and deoxyhemoglobin (Hb) signals from fNIRS. APPROACH First, simulated sinusoidal oscillation signals and four coupled nonlinear systems were employed for performance assessments. Hilbert transform based measurements of hemoglobin phase oxygenation and deoxygenation (hPod) and phase-locking index of hPod (hPodL) were calculated for comparison. Then, the IPE, APE and PCMI indices from resting state fNIRS data of preterm, term infants and adults were calculated to estimate the phase difference and coupling of HbO and Hb. All indices' performance was assessed by the degree of monotonicity (DoM). The box plots and coefficients of variation (CV) were employed to assess the measurements and robustness in the results. MAIN RESULTS In the simulation analysis, IPE and APE can distinguish the phase difference of two sinusoidal oscillation signals. Both hPodL and PCMI can track the strength of two coupled nonlinear systems. Compared to hPodL, the PCMI had higher DoM indices in measuring the coupling of two nonlinear systems. In the fNIRS data analysis, similar to hPod, the IPE and APE can distinguish preterm, term infants, and adults in 0.01-0.05 Hz, 0.05-0.1 Hz, and 0.01-0.1 Hz frequency bands, respectively. PCMI more effectively distinguished the term and preterm infants than hPodL in the 0.05-0.1 Hz frequency band. As symbolic time series measures, the IPE and APE were able to detect the brain developmental changes in subjects of different ages. PCMI can assess the resting-state HbO and Hb coupling changes across different developmental ages, which may reflect the metabolic and neurovascular development. SIGNIFICANCE The symbolic-based methodologies are promising measures for fNIRS in estimating the brain development, especially in assessing newborns' brain developmental status.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States of America
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Developmental changes in cortical sensory processing during wakefulness and sleep. Neuroimage 2018; 178:519-530. [DOI: 10.1016/j.neuroimage.2018.05.075] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 04/05/2018] [Accepted: 05/30/2018] [Indexed: 12/22/2022] Open
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