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Shin JH, Kang MJ, Lee SA. Wearable functional near-infrared spectroscopy for measuring dissociable activation dynamics of prefrontal cortex subregions during working memory. Hum Brain Mapp 2024; 45:e26619. [PMID: 38339822 PMCID: PMC10858338 DOI: 10.1002/hbm.26619] [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: 07/27/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The prefrontal cortex (PFC) has been extensively studied in relation to various cognitive abilities, including executive function, attention, and memory. Nevertheless, there is a gap in our scientific knowledge regarding the functionally dissociable neural dynamics across the PFC during a cognitive task and their individual differences in performance. Here, we explored this possibility using a delayed match-to-sample (DMTS) working memory (WM) task using NIRSIT, a high-density, wireless, wearable functional near-infrared spectroscopy (fNIRS) system. First, upon presentation of the sample stimulus, we observed an immediate signal increase in the ventral (orbitofrontal) region of the anterior PFC, followed by activity in the dorsolateral PFC. After the DMTS test stimulus appeared, the orbitofrontal cortex activated once again, while the rest of the PFC showed overall disengagement. Individuals with higher accuracy showed earlier and sustained activation of the PFC across the trial. Furthermore, higher network efficiency and functional connectivity in the PFC were correlated with individual WM performance. Our study sheds new light on the dynamics of PFC subregional activity during a cognitive task and its potential applicability in explaining individual differences in experimental, educational, or clinical populations. PRACTITIONER POINTS: Wearable functional near-infrared spectroscopy (fNIRS) captured dissociable temporal dynamics across prefrontal subregions during a delayed match-to-sample task. Anterior regions of the orbitofrontal cortex (OFC) activated first during the delay period, followed by the dorsolateral prefrontal cortex (PFC). PFC disengaged overall after the delay, but the OFC reactivated to the test stimulus. Earlier and sustained activation of PFC was associated with better accuracy. Functional connectivity and network efficiency also varied with task performance.
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Affiliation(s)
- Jung Han Shin
- Program of Brain and Cognitive EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea
- Department of Brain and Cognitive SciencesSeoul National UniversitySeoulSouth Korea
| | - Min Jun Kang
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea
| | - Sang Ah Lee
- Department of Brain and Cognitive SciencesSeoul National UniversitySeoulSouth Korea
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Laurence A, Toffa DH, Peng K, Robert M, Bouthillier A, Nguyen DK, Leblond F. Multispectral intraoperative imaging for the detection of the hemodynamic response to interictal epileptiform discharges. BIOMEDICAL OPTICS EXPRESS 2022; 13:6245-6257. [PMID: 36589558 PMCID: PMC9774841 DOI: 10.1364/boe.465699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/03/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
Interictal epileptiform discharges (IEDs) are brief neuronal discharges occurring between seizures in patients with epilepsy. The characterization of the hemodynamic response function (HRF) specific to IEDs could increase the accuracy of other functional imaging techniques to localize epileptiform activity, including functional near-infrared spectroscopy and functional magnetic resonance imaging. This study evaluated the possibility of using an intraoperative multispectral imaging system combined with electrocorticography (ECoG) to measure the average HRF associated with IEDs in eight patients. Inter-patient variability of the HRF is illustrated in terms of oxygenated hemoglobin peak latency, oxygenated hemoglobin increase/decrease following IEDs, and signal-to-noise ratio. A sub-region was identified using an unsupervised clustering algorithm in three patients that corresponded to the most active area identified by ECoG.
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Affiliation(s)
- Audrey Laurence
- Polytechnique Montreal, Department of Engineering Physics, Montréal, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
| | - Dènahin H. Toffa
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
- Centre Hospitalier de l’Université de Montréal, Division of Neurology, Montréal, Canada
| | - Ke Peng
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
| | - Manon Robert
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
| | - Alain Bouthillier
- Centre Hospitalier de l’Université de Montréal, Division of Neurosurgery, Montréal, Canada
| | - Dang K. Nguyen
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
- Centre Hospitalier de l’Université de Montréal, Division of Neurology, Montréal, Canada
| | - Frederic Leblond
- Polytechnique Montreal, Department of Engineering Physics, Montréal, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Canada
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Cai Z, Pellegrino G, Lina J, Benali H, Grova C. Hierarchical Bayesian modeling of the relationship between task-related hemodynamic responses and cortical excitability. Hum Brain Mapp 2022; 44:876-900. [PMID: 36250709 PMCID: PMC9875942 DOI: 10.1002/hbm.26107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/10/2022] [Accepted: 09/18/2022] [Indexed: 01/28/2023] Open
Abstract
Investigating the relationship between task-related hemodynamic responses and cortical excitability is challenging because it requires simultaneous measurement of hemodynamic responses while applying noninvasive brain stimulation. Moreover, cortical excitability and task-related hemodynamic responses are both associated with inter-/intra-subject variability. To reliably assess such a relationship, we applied hierarchical Bayesian modeling. This study involved 16 healthy subjects who underwent simultaneous Paired Associative Stimulation (PAS10, PAS25, Sham) while monitoring brain activity using functional Near-Infrared Spectroscopy (fNIRS), targeting the primary motor cortex (M1). Cortical excitability was measured by Motor Evoked Potentials (MEPs), and the motor task-related hemodynamic responses were measured using fNIRS 3D reconstructions. We constructed three models to investigate: (1) PAS effects on the M1 excitability, (2) PAS effects on fNIRS hemodynamic responses to a finger tapping task, and (3) the correlation between PAS effects on M1 excitability and PAS effects on task-related hemodynamic responses. Significant increase in cortical excitability was found following PAS25, whereas a small reduction of the cortical excitability was shown after PAS10 and a subtle increase occurred after sham. Both HbO and HbR absolute amplitudes increased after PAS25 and decreased after PAS10. The probability of the positive correlation between modulation of cortical excitability and hemodynamic activity was 0.77 for HbO and 0.79 for HbR. We demonstrated that PAS stimulation modulates task-related cortical hemodynamic responses in addition to M1 excitability. Moreover, the positive correlation between PAS modulations of excitability and hemodynamics brought insight into understanding the fundamental properties of cortical function and cortical excitability.
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Affiliation(s)
- Zhengchen Cai
- Multimodal Functional Imaging Lab, Department of PhysicsConcordia UniversityMontréalQuébecCanada,PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Giovanni Pellegrino
- Epilepsy Program, Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada,Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Jean‐Marc Lina
- Département de Génie ElectriqueÉcole de Technologie SupérieureMontréalQuébecCanada,Centre De Recherches En MathématiquesMontréalQuébecCanada
| | - Habib Benali
- PERFORM CentreConcordia UniversityMontréalQuébecCanada,Centre De Recherches En MathématiquesMontréalQuébecCanada,Electrical and Computer Engineering Department, Concordia UniversityMontréalCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of PhysicsConcordia UniversityMontréalQuébecCanada,PERFORM CentreConcordia UniversityMontréalQuébecCanada,Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada,Centre De Recherches En MathématiquesMontréalQuébecCanada
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Mann‐Krzisnik D, Mitsis GD. Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition. Hum Brain Mapp 2022; 43:4045-4073. [PMID: 35567768 PMCID: PMC9374895 DOI: 10.1002/hbm.25902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/11/2022] Open
Abstract
The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.
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Affiliation(s)
- Dylan Mann‐Krzisnik
- Graduate Program in Biological and Biomedical EngineeringMcGill UniversityMontréalQuebecCanada
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Cai Z, Machado A, Chowdhury RA, Spilkin A, Vincent T, Aydin Ü, Pellegrino G, Lina JM, Grova C. Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean. Sci Rep 2022; 12:2316. [PMID: 35145148 PMCID: PMC8831678 DOI: 10.1038/s41598-022-06082-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40\documentclass[12pt]{minimal}
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\begin{document}$$\text {cm}^2$$\end{document}cm2 along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE.
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Affiliation(s)
- Zhengchen Cai
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.
| | - Alexis Machado
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
| | - Rasheda Arman Chowdhury
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
| | - Amanda Spilkin
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
| | - Thomas Vincent
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.,Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada.,Centre de médecine préventive et d'activité physique, Montréal Heart Institute, Montréal, Canada
| | - Ümit Aydin
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.,MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Giovanni Pellegrino
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Marc Lina
- École de technologie supérieure de l'Université du Québec, Montréal, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada
| | - Christophe Grova
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.,Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada.,Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada
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