1
|
Liu F, Chi X, Yu D. Reduced inhibition control ability in children with ADHD due to coexisting learning disorders: an fNIRS study. Front Psychiatry 2024; 15:1326341. [PMID: 38832323 PMCID: PMC11146205 DOI: 10.3389/fpsyt.2024.1326341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 04/25/2024] [Indexed: 06/05/2024] Open
Abstract
Introduction Inhibition control, as the core component of executive function, might play a crucial role in the understanding of attention deficit/hyperactivity disorder (ADHD) and specific learning disorders (SLD). Inhibition control deficits have been observed in children with ADHD or SLD. This study sought to test in a multi-modal fashion (i.e., behavior and plus brain imaging) whether inhibition control abilities would be further deteriorated in the ADHD children due to the comorbidity of SLD. Method A total number of 90 children (aged 6-12 years) were recruited, including 30 ADHD, 30 ADHD+SLD (children with the comorbidity of ADHD and SLD), and 30 typically developing (TD) children. For each participant, a 44-channel functional near infrared spectroscopy (fNIRS) equipment was first adopted to capture behavioral and cortical hemodynamic responses during a two-choice Oddball task (a relatively new inhibition control paradigm). Then, 50 metrics were extracted, including 6 behavioral metrics (i.e., OddballACC, baselineACC, totalACC, OddballRT, baselineRT, and totalRT) and 44 beta values in 44 channels based on general linear model. Finally, differences in those 50 metrics among the TD, ADHD, and ADHD+SLD children were analyzed. Results Findings showed that: (1) OddballACC (i.e., the response accuracy in deviant stimuli) is the most sensitive metric in identifying the differences between the ADHD and ADHD+SLD children; and (2) The ADHD+SLD children exhibited decreased behavioral response accuracy and brain activation level in some channels (e.g., channel CH35) than both the ADHD and TD children. Discussion Findings seem to support that inhibition control abilities would be further decreased in the ADHD children due to the comorbidity of SLD.
Collapse
Affiliation(s)
- Fulin Liu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xia Chi
- Department of Child Health Care, Women’s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, Jiangsu, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Henan Provincial Medical Key Lab of Child Developmental Behavior and Learning, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| |
Collapse
|
2
|
Yoo SH, Huang G, Hong KS. Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks. Bioengineering (Basel) 2023; 10:685. [PMID: 37370616 DOI: 10.3390/bioengineering10060685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary.
Collapse
Affiliation(s)
- So-Hyeon Yoo
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
| | - Guanghao Huang
- Institute for Future, School of Automation, Qingdao University, Qingdao 266071, China
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea
- Institute for Future, School of Automation, Qingdao University, Qingdao 266071, China
| |
Collapse
|
3
|
Liu D, Zhang Y, Zhang P, Li T, Li Z, Zhang L, Gao F. Deep-learning informed Kalman filtering for priori-free and real-time hemodynamics extraction in functional near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:4787-4801. [PMID: 36187239 PMCID: PMC9484432 DOI: 10.1364/boe.467943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 06/16/2023]
Abstract
Separation of the physiological interferences and the neural hemodynamics has been a vitally important task in the realistic implementation of functional near-infrared spectroscopy (fNIRS). Although many efforts have been devoted, the established solutions to this issue additionally rely on priori information on the interferences and activation responses, such as time-frequency characteristics and spatial patterns, etc., also hindering the realization of real-time. To tackle the adversity, we herein propose a novel priori-free scheme for real-time physiological interference suppression. This method combines the robustness of deep-leaning-based interference characterization and adaptivity of Kalman filtering: a long short-term memory (LSTM) network is trained with the time-courses of the absorption perturbation baseline for interferences profiling, and successively, a Kalman filtering process is applied with reference to the noise prediction for real-time activation extraction. The proposed method is validated using both simulated dynamic data and in-vivo experiments, showing the comprehensively improved performance and promisingly appended superiority achieved in the purely data-driven way.
Collapse
Affiliation(s)
- Dongyuan Liu
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Yao Zhang
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Pengrui Zhang
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Tieni Li
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Zhiyong Li
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Limin Zhang
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
- Tianjin Key laboratory of Biomedical Detecting Techniques and Instruments, Tianjin 300072, China
| | - Feng Gao
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
- Tianjin Key laboratory of Biomedical Detecting Techniques and Instruments, Tianjin 300072, China
| |
Collapse
|
4
|
Scholkmann F, Tachtsidis I, Wolf M, Wolf U. Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain. NEUROPHOTONICS 2022; 9:030801. [PMID: 35832785 PMCID: PMC9272976 DOI: 10.1117/1.nph.9.3.030801] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/07/2022] [Indexed: 05/15/2023]
Abstract
In this Outlook paper, we explain why an accurate physiological interpretation of functional near-infrared spectroscopy (fNIRS) neuroimaging signals is facilitated when systemic physiological activity (e.g., cardiorespiratory and autonomic activity) is measured simultaneously by employing systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). The rationale for SPA-fNIRS is twofold: (i) SPA-fNIRS enables a more complete interpretation and understanding of the fNIRS signals measured at the head since they contain components originating from neurovascular coupling and from systemic physiological sources. The systemic physiology signals measured with SPA-fNIRS can be used for regressing out physiological confounding components in fNIRS signals. Misinterpretations can thus be minimized. (ii) SPA-fNIRS enables to study the embodied brain by linking the brain with the physiological state of the entire body, allowing novel insights into their complex interplay. We envisage the SPA-fNIRS approach will become increasingly important in the future.
Collapse
Affiliation(s)
- Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Neonatology Research, Department of Neonatology, Zurich, Switzerland
| | - Ilias Tachtsidis
- University College London, Biomedical Optics Research Laboratory, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Martin Wolf
- University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Neonatology Research, Department of Neonatology, Zurich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| |
Collapse
|
5
|
Al-Shargie F, Katmah R, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. Stress management using fNIRS and binaural beats stimulation. BIOMEDICAL OPTICS EXPRESS 2022; 13:3552-3575. [PMID: 35781942 PMCID: PMC9208616 DOI: 10.1364/boe.455097] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/21/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.
Collapse
Affiliation(s)
- Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Rateb Katmah
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Usman Tariq
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Fabio Babiloni
- Department Molecular Medicine, University of Sapienza Rome, 00185 Rome, Italy
| | - Fadwa Al-Mughairbi
- Department of Clinical Psychology, College of Medicines and Health Sciences, UAE University, Al Ain, United Arab Emirates
| | - Hasan Al-Nashash
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| |
Collapse
|
6
|
Abdalmalak A, Novi SL, Kazazian K, Norton L, Benaglia T, Slessarev M, Debicki DB, Lawrence KS, Mesquita RC, Owen AM. Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy. Front Neurosci 2022; 16:803297. [PMID: 35350556 PMCID: PMC8957952 DOI: 10.3389/fnins.2022.803297] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/07/2022] [Indexed: 12/11/2022] Open
Abstract
Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.
Collapse
Affiliation(s)
- Androu Abdalmalak
- Department of Physiology and Pharmacology, Western University, London, ON, Canada
- Brain and Mind Institute, Western University, London, ON, Canada
- *Correspondence: Androu Abdalmalak,
| | - Sergio L. Novi
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas, Brazil
- *Correspondence: Androu Abdalmalak,
| | - Karnig Kazazian
- Brain and Mind Institute, Western University, London, ON, Canada
| | - Loretta Norton
- Department of Psychology, King’s University College at Western University, London, ON, Canada
| | - Tatiana Benaglia
- Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas, Brazil
| | - Marat Slessarev
- Clinical Neurological Sciences, Western University, London, ON, Canada
| | - Derek B. Debicki
- Brain and Mind Institute, Western University, London, ON, Canada
- Clinical Neurological Sciences, Western University, London, ON, Canada
| | - Keith St. Lawrence
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Rickson C. Mesquita
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas, Brazil
| | - Adrian M. Owen
- Department of Physiology and Pharmacology, Western University, London, ON, Canada
- Brain and Mind Institute, Western University, London, ON, Canada
- Department of Psychology, Western University, London, ON, Canada
| |
Collapse
|
7
|
Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli. Sci Rep 2021; 11:23457. [PMID: 34873185 PMCID: PMC8648757 DOI: 10.1038/s41598-021-02076-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/18/2021] [Indexed: 11/08/2022] Open
Abstract
Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within the VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.
Collapse
|
8
|
Liu D, Zhang P, Zhang Y, Bai L, Gao F. Suppressing physiological interferences and physical noises in functional diffuse optical tomography via tandem inversion filtering and LSTM classification. OPTICS EXPRESS 2021; 29:29275-29291. [PMID: 34615040 DOI: 10.1364/oe.433917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
For performance enhancement of functional diffuse optical tomography (fDOT), we propose a tandem method that takes advantage of the inversion filtering and the long short term memory (LSTM) classification to simultaneously suppress the physiological interferences and physical noises in fDOT. In the former phase, the absorption perturbation maps over the scalp-skull (SS) and cerebral-cortex (CC) layers are firstly pre-reconstructed using a two-layer topography scheme. Then, the recovered SS-map is inversed into measurement space by the forward calculation to estimate the intensity changes associated with the physiological interferences. Finally, the raw measurements are adaptively filtered with reference to the estimated intensity changes for accomplishing the model-based full three-dimension (3D) reconstruction. In the later phase, for further removing the randomly distributed physical noises, mainly instrumental noise, a LSTM network based fusion strategy is applied, where a pixel-wise binary mask of "activated" or "inactive" state is generated by performing LSTM classification and then fused with the 3D reconstruction. The results of the simulative investigation and in-vivo experiment show the proposed tandem scheme achieves improved performance in fDOT using a cost-effective and physically explicable way. Thus, the proposed method can be promisingly applied in real-time neuroimaging to acquire cortical neural activation information with improved reliability, quantification and resolution.
Collapse
|
9
|
Zhang F, Cheong D, Khan AF, Chen Y, Ding L, Yuan H. Correcting physiological noise in whole-head functional near-infrared spectroscopy. J Neurosci Methods 2021; 360:109262. [PMID: 34146592 DOI: 10.1016/j.jneumeth.2021.109262] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 05/20/2021] [Accepted: 06/14/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage. NEW METHOD We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated. RESULTS After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas. COMPARISON WITH EXISTING METHODS Analysis showed that our method improves upon the four established preprocessing methods in the literature. CONCLUSIONS An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.
Collapse
Affiliation(s)
- Fan Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA
| | - Daniel Cheong
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA
| | - Ali F Khan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA
| | - Yuxuan Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA; Institute for Biomedical Engineering, Science and Technology, University of Oklahoma, Norman, OK, USA
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA; Institute for Biomedical Engineering, Science and Technology, University of Oklahoma, Norman, OK, USA.
| |
Collapse
|
10
|
Chan YL, Ung WC, Lim LG, Lu CK, Kiguchi M, Tang TB. Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1691-1701. [PMID: 32746314 DOI: 10.1109/tnsre.2020.3007589] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.
Collapse
|
11
|
Wyser D, Mattille M, Wolf M, Lambercy O, Scholkmann F, Gassert R. Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics. NEUROPHOTONICS 2020; 7:035011. [PMID: 33029548 PMCID: PMC7523733 DOI: 10.1117/1.nph.7.3.035011] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 09/04/2020] [Indexed: 05/20/2023]
Abstract
Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic "noise" by subtracting the signal measured at a short source-detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings.
Collapse
Affiliation(s)
- Dominik Wyser
- ETH Zurich, Department of Health Sciences and Technology, Rehabilitation Engineering Laboratory, Zurich, Switzerland
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zurich, Switzerland
| | - Michelle Mattille
- ETH Zurich, Department of Health Sciences and Technology, Rehabilitation Engineering Laboratory, Zurich, Switzerland
| | - Martin Wolf
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zurich, Switzerland
| | - Olivier Lambercy
- ETH Zurich, Department of Health Sciences and Technology, Rehabilitation Engineering Laboratory, Zurich, Switzerland
| | - Felix Scholkmann
- University Hospital Zurich, University of Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zurich, Switzerland
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Roger Gassert
- ETH Zurich, Department of Health Sciences and Technology, Rehabilitation Engineering Laboratory, Zurich, Switzerland
| |
Collapse
|
12
|
Schumacher FK, Steinborn C, Weiller C, Schelter BO, Reinhard M, Kaller CP. The impact of physiological noise on hemodynamic-derived estimates of directed functional connectivity. Brain Struct Funct 2019; 224:3145-3157. [DOI: 10.1007/s00429-019-01954-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 08/31/2019] [Indexed: 11/29/2022]
|