Dynamic causal modelling for functional near-infrared spectroscopy

Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives

Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.

Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

Surface-based integration approach for fNIRS-fMRI reliability assessment

INTRODUCTION

Studies integrating functional near-infrared spectroscopy (fNIRS) with functional MRI (fMRI) employ heterogeneous methods in defining common regions of interest in which similarities are assessed. Therefore, spatial agreement and temporal correlation may not be reproducible across studies. In the present work, we address this issue by proposing a novel method for integration and analysis of fNIRS and fMRI over the cortical surface.

MATERIALS AND METHODS

Eighteen healthy volunteers (age mean±SD 30.55 ± 4.7, 7 males) performed a motor task during non-simultaneous fMRI and fNIRS acquisitions. First, fNIRS and fMRI data were integrated by projecting subject- and group-level source maps over the cortical surface mesh to define anatomically constrained functional ROIs (acfROI). Next, spatial agreement and temporal correlation were quantified as Dice Coefficient (DC) and Pearson's correlation coefficient between fNIRS-fMRI in the acfROIs.

RESULTS

Subject-level results revealed moderate to substantial spatial agreement (DC range 0.43 - 0.64), confirmed at the group-level only for blood oxygenation level-dependent (BOLD) signal vs. HbO2 (0.44 - 0.69), while lack of agreement was found for BOLD vs. HbR in some instances (0.05 - 0.49). Subject-level temporal correlation was moderate to strong (0.79 - 0.85 for BOLD vs. HbO2 and -0.62 to -0.72 for BOLD vs. HbR), while an overall strong correlation was found for group-level results (0.95 - 0.98 for BOLD vs. HbO2 and -0.91 to -0.94 for BOLD vs. HbR).

CONCLUSION

The proposed method directly compares fNIRS and fMRI by projecting individual source maps to the cortical surface. Our results indicate spatial and temporal correspondence between fNIRS and fMRI, and promotes the use of fNIRS when more ecological acquision settings are required, such as longitudinal monitoring of brain activity before and after rehabilitation.

Using functional near-infrared spectroscopy to study the early developing brain: future directions and new challenges

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.

Impact of Anatomical Variability on Sensitivity Profile in fNIRS-MRI Integration

Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source-detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation.

Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data

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.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Modulation of the prefrontal blood oxygenation response to intermittent theta-burst stimulation in depression: A sham-controlled study with functional near-infrared spectroscopy

OBJECTIVE

To better understand the neural mechanisms behind the effect of intermittent theta-burst stimulation (iTBS), we investigated how the prefrontal blood oxygenation response measured by changes in oxygenated haemoglobin (oxy-Hb) was modulated during a sham-controlled iTBS treatment course, and whether this was related to depressive symptom change.

METHODS

In this randomised, double-blind study, patients with ongoing treatment-resistant depression received either active (n = 18) or sham (n = 21) iTBS over the dorsomedial prefrontal cortex for ten to fifteen days with two sessions daily. Event-related functional near-infrared spectroscopy (fNIRS) was measured during each iTBS train, and resting-state oxy-Hb was compared before and after each iTBS session at the first, fifth, and last treatment day.

RESULTS

Patients receiving active iTBS had an increase of the event-related oxy-Hb response compared to the sham group on the fifth (bilateral prefrontal cortices p < .001) and last (left prefrontal p = .007, right prefrontal p = .025) treatment day. Resting-state analysis showed suppressed oxy-Hb change in active iTBS compared to sham iTBS on the last treatment day (p = .024). Oxy-Hb change was unrelated to depressive symptom change (p = .474).

CONCLUSIONS

This study describes a modulation of the blood oxygenation response over the prefrontal cortex that was built up during the course of active iTBS treatment in depression.

Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy

OBJECTIVE

Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient for calculation.

APPROACH

To overcome these two issues, a delay symbolic phase transfer entropy (dSPTE) is proposed by developing traditional transfer entropy (TE) to estimate EC for fNIRS. Firstly, the phase time sequence was obtained from the original sequence by the Hilbert transform and state-space reconstruction was realized using a uniform embedding scheme. Then, a symbolization technique was applied based on a neural-gas algorithm to improve its noise robustness. Finally, the EC was calculated on multiple time delay scales to match different inter-region neurotransmission delays.

MAIN RESULTS

A linear AR model, a nonlinear model and a multivariate hybrid model were introduced to simulate the performance of dSPTE, and the results showed that the accuracy of dSPTE was the highest, up to 74.27%, and specificity was 100% which means no false connectivity. The results confirmed that the dSPTE method realized better noise robustness, higher accuracy, and correct identification even if there was a long delay between series. Finally, we applied dSPTE to fNIRS dataset to analyse the EC during the finger-tapping task, the results showed that EC strength of task state significantly increased compared with the resting state.

SIGNIFICANCE

The proposed dSPTE method is a promising way to measure the EC for fNIRS. It incorporates the phase information TE with a symbolic process for fNIRS analysis for the first time. It has been confirmed to be noise robust and suitable for the complex network with different coupling delays.

Identification of the Cognitive Interference Effect Related to Stroop Stimulation: Using Dynamic Causal Modeling of Effective Connectivity in Functional Near-Infrared Spectroscopy (fNIRS)

Background:

The Stroop test is a well-known model to denote the decline in performance under the incongruent condition, which requires selective attention and control of competitive responses. Functional near-infrared spectroscopy can identify activated brain regions associated with the Stroop interference effect.

Objective:

This research aims to identify the neural correlates associated with the Stroop tasks within the brain activated regions.

Materials and Methods:

In this cross sectional study, twelve right-handed healthy controls were investigated by means of a multi-channels fNIRS unit during the execution of the Stroop test. Effective connectivity changes in the prefrontal cortex between Stroop attentional conflict and rest states were calculated using DCM approach to investigate (1) areas known for selective attention and (2) analyze inter-network functional connectivity strength (FCS) by selecting several brain functional networks.

Results:

The results indicated that an increased activity was recorded in the LDLPFC during incongruent condition, while under neutral condition, the increase in activity was even more pronounced in those areas. Effect of Stroop interference associated with significant consistent causes an increase in the RDLPFC to DMPFC, LDLPFC to DMPFC and LDLPFC to RPFC effective connectivity strengths.

Conclusion:

This study showed the use of DCM algorithm for fNIRS data with respect to fMRI has provided additional information about the directional connectivity and causal interactions in LPFC networks during a conflict processing. Eventually, high temporal resolution fNIRS can be a promising tool for monitoring functional brain activation under the cognitive paradigms in neurological research and psychotherapy applications.

Hemodynamics Analysis of Patients With Mild Cognitive Impairment During Working Memory Tasks

Diagnosis of dementia in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of Alzheimer disease (AD). In this paper, we aim to classify MCI patients from healthy controls (HC) during working memory tasks using functional near-infrared spectroscopy (fNIRS). To achieve this objective, t-values and correlation coefficients are calculated to find the region of interest (ROI) channels and brain connectivity. From the ROI channels averaged over subjects, features (mean and slope) of hemodynamic responses were extracted for classification. Extracted features were labelled as two classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (SVM). The classification accuracies were 73.08 % with LDA and 71.15 % with SVM. The results show that there are significant differences in the hemodynamic responses (HR) between MCI patients and healthy controls. Therefore, these results suggest a possibility of using fNIRS as a diagnostic tool for MCI patients.

The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience

The past few decades have seen a rapid increase in the use of functional near-infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state-of-the-art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience.

Atypical Dynamic-Connectivity Recruitment in Attention-Deficit/Hyperactivity Disorder Children: An Insight Into Task-Based Dynamic Connectivity Through an fNIRS Study

Connectivity between brain regions has been redefined beyond a stationary state. Even when a person is in a resting state, brain connectivity dynamically shifts. However, shifted brain connectivity under externally evoked stimulus is still little understood. The current study, therefore, focuses on task-based dynamic functional-connectivity (FC) analysis of brain signals measured by functional near-infrared spectroscopy (fNIRS). We hypothesize that a stimulus may influence not only brain connectivity but also the occurrence probabilities of task-related and task-irrelevant connectivity states. fNIRS measurement (of the prefrontal-to-inferior parietal lobes) was conducted on 21 typically developing (TD) and 21 age-matched attention-deficit/hyperactivity disorder (ADHD) children performing an inhibitory control task, namely, the Go/No-Go (GNG) task. It has been reported that ADHD children lack inhibitory control; differences between TD and ADHD children in terms of task-based dynamic FC were also evaluated. Four connectivity states were found to occur during the temporal task course. Two dominant connectivity states (states 1 and 2) are characterized by strong connectivities within the frontoparietal network (occurrence probabilities of 40%-56% and 26%-29%), and presumptively interpreted as task-related states. A connectivity state (state 3) shows strong connectivities in the bilateral medial frontal-to-parietal cortices (occurrence probability of 7-15%). The strong connectivities were found at the overlapped regions related the default mode network (DMN). Another connectivity state (state 4) visualizes strong connectivities in all measured regions (occurrence probability of 10%-16%). A global effect coming from cerebral vascular may highly influence this connectivity state. During the GNG stimulus interval, the ADHD children tended to show decreased occurrence probability of the dominant connectivity state and increased occurrence probability of other connectivity states (states 3 and 4). Bringing a new perspective to explain neuropathophysiology, these findings suggest atypical dynamic network recruitment to accommodate task demands in ADHD children.

Maternal speech shapes the cerebral frontotemporal network in neonates: A hemodynamic functional connectivity study

Language development and the capacity for communication in infants are predominantly supported by their mothers, beginning when infants are still in utero. Although a mother's speech should thus have a significant impact on her neonate's brain, neurocognitive evidence for this hypothesis remains elusive. The present study examined 37 neonates using near-infrared spectroscopy and observed the interactions between multiple cortical regions while neonates heard speech spoken by their mothers or by strangers. We analyzed the functional connectivity between regions whose response-activation patterns differed between the two types of speakers. We found that when hearing their mothers' speech, functional connectivity was enhanced in both the neonatal left and right frontotemporal networks. On the left it was enhanced between the inferior/middle frontal gyrus and the temporal cortex, while on the right it was enhanced between the frontal pole and temporal cortex. In particular, the frontal pole was more strongly connected to the left supramarginal area when hearing speech from mothers. These enhanced frontotemporal networks connect areas that are associated with language (left) and voice processing (right) at later stages of development. We suggest that these roles are initially fostered by maternal speech.

Remote effects on corticospinal excitability during motor execution and motor imagery

We investigated the remote effect on corticospinal excitability of resting left and right hand muscles during motor execution and motor imagery when performing left or right foot plantar flexion. Fifteen right-handed subjects performed two conditions with three tasks: Condition (Motor Execution (ME) vs. Motor Imagery (MI)): Task (Control, Ipsilateral, and Contralateral). From the left and right first dorsal interosseous, motor evoked potentials (MEPs) elicited by a single-pulse transcranial magnetic stimulation (TMS) to the left or right primary motor cortices (M1) were recorded under all six trials. MEP amplitudes were significantly larger under the ME than MI condition, irrespective of hands and tasks. MEP amplitudes were also the largest during the Contralateral tasks, irrespective of the condition and hands. The correlation analysis showed that MEP amplitudes were significantly correlated between ME and MI conditions with both left and right hands. Our results indicate that the magnitude of the remote effect on corticospinal excitability of hand muscles differs between motor execution and motor imagery, and between ipsi- and contralateral limbs, when performing foot plantar flexion.

Reconstruction of effective connectivity in the case of asymmetric phase distributions

BACKGROUND

The interaction of different brain regions is supported by transient synchronization between neural oscillations at different frequencies. Different measures based on synchronization theory are used to assess the strength of the interactions from experimental data. One method of estimating the effective connectivity between brain regions, within the framework of the theory of weakly coupled phase oscillators, was implemented in Dynamic Causal Modelling (DCM) for phase coupling (Penny et al., 2009). However, the results of such an approach strongly depend on the observables used to reconstruct the equations (Kralemann et al., 2008). In particular, an asymmetric distribution of the observables could result in a false estimation of the effective connectivity between the network nodes.

NEW METHOD

In this work we built a new modelling part into DCM for phase coupling, and extended it with a distortion function that accommodates departures from purely sinusoidal oscillations.

RESULTS

By analysing numerically generated data sets with an asymmetric phase distribution, we demonstrated that the extended DCM for phase coupling with the additional modelling component, correctly estimates the coupling functions.

COMPARISON WITH EXISTING METHODS

The new method allows for different intrinsic frequencies among coupled neuronal populations and provides results that do not depend on the distribution of the observables.

CONCLUSIONS

The proposed method can be used to analyse effective connectivity between brain regions within and between different frequency bands, to characterize m:n phase coupling, and to unravel underlying mechanisms of the transient synchronization.

Functional near-infrared spectroscopy for monitoring macaque cerebral motor activity during voluntary movements without head fixation

We developed an fNIRS system for monitoring macaque cerebral motor activity during voluntary movements without head fixation. fNIRS data at 27 channels in 7.5 mm spatial interval were calibrated by simulating light propagation through the macaque cranial tissues. The subject was instructed to repeatedly (75 times) retrieve a food pellet with alternating left or right hands from a food well for each session. We detected significant increases in oxygenated hemoglobin (Hb) and decrease in deoxygenated Hb in the primary motor area (M1) contralateral to the hand used. In more rostral and ventral regions in both hemispheres, the hemodynamic similarly changed regardless of used hand. Direct feeding to the mouth eliminated activity in the hand M1 whereas that at bilateral ventral regions (mouth M1 area) remained. Statistical analyses for the hemodynamics between left/right-hand use revealed the location of each hand M1 in either hemisphere. In these regions, the maximum amplitude and time of the maximum amplitude in the hemodynamic response evoked by food retrieval were highly correlated with the time associated with food retrieval. We could assign each channel to an appropriate functional motor area, providing proof of principle for future studies involving brain damage models in freely moving macaque monkeys.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

OBJECTIVE

Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown.

APPROACH

Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF).

MAIN RESULTS

We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements.

SIGNIFICANCE

The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies.

fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data.

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Connectivity studies give important insights into infant brain development.

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fNIRS is a valuable method for infancy studies, but can we analyse connectivity?

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On fMRI-fNIRS acquired simultaneously, we estimate effective connectivity with DCM.

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We showed high correspondence of DCM values between fMRI and fNIRS data.

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We validated DCM on fNIRS infant data, providing guidance for future projects.

Inhibition or facilitation? Modulation of corticospinal excitability during motor imagery

Motor imagery (MI) is the mental simulation of an action without any overt movement. Functional evidences show that brain activity during MI and motor execution (ME) largely overlaps. However, the role of the primary motor cortex (M1) during MI is controversial. Effective connectivity techniques show a facilitation on M1 during ME and an inhibition during MI, depending on whether an action should be performed or suppressed. Conversely, Transcranial Magnetic Stimulation (TMS) studies report facilitatory effects during both ME and MI. The present TMS study shed light on MI mechanisms, by manipulating the instructions given to the participants. In both Experimental and Control groups, participants were asked to mentally simulate a finger-thumb opposition task, but only the Experimental group received the explicit instruction to avoid any unwanted fingers movements. The amplitude of motor evoked potentials (MEPs) to TMS during MI was compared between the two groups. If the M1 facilitation actually pertains to MI per se, we should have expected to find it, irrespective of the instructions. Contrariwise, we found opposite results, showing facilitatory effects (increased MEPs amplitude) in the Control group and inhibitory effects (decreased MEPs amplitude) in the Experimental group. Control experiments demonstrated that the inhibitory effect was specific for the M1 contralateral to the hand performing the MI task and that the given instructions did not compromise the subjects' MI abilities. The present findings suggest a crucial role of motor inhibition when a "pure" MI task is performed and the subjects are explicitly instructed to avoid overt movements.

Imperial College near infrared spectroscopy neuroimaging analysis framework

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.

Mapping effective connectivity within cortical networks with diffuse optical tomography

Understanding how cortical networks interact in response to task demands is important both for providing insight into the brain's processing architecture and for managing neurological diseases and mental disorders. High-density diffuse optical tomography (HD-DOT) is a neuroimaging technique that offers the significant advantages of having a naturalistic, acoustically controllable environment and being compatible with metal implants, neither of which is possible with functional magnetic resonance imaging. We used HD-DOT to study the effective connectivity and assess the modulatory effects of speech intelligibility and syntactic complexity on functional connections within the cortical speech network. To accomplish this, we extend the use of a generalized psychophysiological interaction (PPI) analysis framework. In particular, we apply PPI methods to event-related HD-DOT recordings of cortical oxyhemoglobin activity during auditory sentence processing. We evaluate multiple approaches for selecting cortical regions of interest and for modeling interactions among these regions. Our results show that using subject-based regions has minimal effect on group-level connectivity maps. We also demonstrate that incorporating an interaction model based on estimated neural activity results in significantly stronger effective connectivity. Taken together our findings support the use of HD-DOT with PPI methods for noninvasively studying task-related modulations of functional connectivity.

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

Diffuse optical tomography (DOT) measures concentration changes in both oxy- and deoxyhemoglobin providing three-dimensional images of local brain activations. A pilot study, which compares both DOT and functional magnetic resonance imaging (fMRI) volumes through t-maps given by canonical statistical parametric mapping (SPM) processing for both data modalities, is presented. The DOT series were processed using a method that is based on a Bayesian filter application on raw DOT data to remove physiological changes and minimum description length application index to select a number of singular values, which reduce the data dimensionality during image reconstruction and adaptation of DOT volume series to normalized standard space. Therefore, statistical analysis is performed with canonical SPM software in the same way as fMRI analysis is done, accepting DOT volumes as if they were fMRI volumes. The results show the reproducibility and ruggedness of the method to process DOT series on group analysis using cognitive paradigms on the prefrontal cortex. Difficulties such as the fact that scalp-brain distances vary between subjects or cerebral activations are difficult to reproduce due to strategies used by the subjects to solve arithmetic problems are considered. T-images given by fMRI and DOT volume series analyzed in SPM show that at the functional level, both DOT and fMRI measures detect the same areas, although DOT provides complementary information to fMRI signals about cerebral activity.

Near-Infrared Spectroscopy - Electroencephalography-Based Brain-State-Dependent Electrotherapy: A Computational Approach Based on Excitation-Inhibition Balance Hypothesis

Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The poststroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this hypothesis and theory article, we propose a computational approach based on excitation–inhibition (E–I) balance hypothesis to objectively quantify the poststroke individual brain state using online fNIRS–EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local E–I (that is the ratio of Glutamate/GABA), which may be targeted with NIBS using a computational pipeline that includes individual “forward models” to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons, which can be captured with E–I-based brain models. Furthermore, E–I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing, which can then be implicated in changes in function. We first review the evidence that shows how this local imbalance between E–I leading to functional dysfunction can be restored in targeted sites with NIBS (motor cortex and somatosensory cortex) resulting in large-scale plastic reorganization over the cortex, and probably facilitating recovery of functions. Second, we show evidence how BSDE based on E–I balance hypothesis may target a specific brain site or network as an adjuvant treatment. Hence, computational neural mass model-based integration of neurostimulation with online neuroimaging systems may provide less ambiguous, robust optimization of NIBS, and its application in neurological conditions and disorders across individual patients.

Functional near infrared spectroscopy as a probe of brain function in people with prolonged disorders of consciousness

Near infrared spectroscopy (NIRS) is a non-invasive technique which measures changes in brain tissue oxygenation. NIRS has been used for continuous monitoring of brain oxygenation during medical procedures carrying high risk of iatrogenic brain ischemia and also has been adopted by cognitive neuroscience for studies on executive and cognitive functions. Until now, NIRS has not been used to detect residual cognitive functions in patients with prolonged disorders of consciousness (pDOC). In this study we aimed to evaluate the brain function of patients with pDOC by using a motor imagery task while recording NIRS. We also collected data from a group of age and gender matched healthy controls while they carried out both real and imagined motor movements to command. We studied 16 pDOC patients in total, split into two groups: five had a diagnosis of Vegetative state/Unresponsive Wakefulness State, and eleven had a diagnosis of Minimally Conscious State. In the control subjects we found a greater oxy-haemoglobin (oxyHb) response during real movement compared with imagined movement. For the between group comparison, we found a main effect of hemisphere, with greater depression of oxyHb signal in the right > left hemisphere compared with rest period for all three groups. A post-hoc analysis including only the two pDOC patient groups was also significant suggesting that this effect was not just being driven by the control subjects. This study demonstrates for the first time the feasibility of using NIRS for the assessment of brain function in pDOC patients using a motor imagery task.

Supplementary motor area deactivation impacts the recovery of hand function from severe peripheral nerve injury

Although some patients have successful peripheral nerve regeneration, a poor recovery of hand function often occurs after peripheral nerve injury. It is believed that the capability of brain plasticity is crucial for the recovery of hand function. The supplementary motor area may play a key role in brain remodeling after peripheral nerve injury. In this study, we explored the activation mode of the supplementary motor area during a motor imagery task. We investigated the plasticity of the central nervous system after brachial plexus injury, using the motor imagery task. Results from functional magnetic resonance imaging showed that after brachial plexus injury, the motor imagery task for the affected limbs of the patients triggered no obvious activation of bilateral supplementary motor areas. This result indicates that it is difficult to excite the supplementary motor areas of brachial plexus injury patients during a motor imagery task, thereby impacting brain remodeling. Deactivation of the supplementary motor area is likely to be a serious problem for brachial plexus injury patients in terms of preparing, initiating and executing certain movements, which may be partly responsible for the unsatisfactory clinical recovery of hand function.

Sensor space group analysis for fNIRS data

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) is a method for monitoring hemoglobin responses using optical probes placed on the scalp. fNIRS spatial resolution is limited by the distance between channels defined as a pair of source and detector, and channel positions are often inconsistent across subjects. These challenges can lead to less accurate estimate of group level effects from channel-specific measurements.

NEW METHOD

This paper addresses this shortcoming by applying random-effects analysis using summary statistics to interpolated fNIRS topographic images. Specifically, we generate individual contrast images containing the experimental effects of interest in a canonical scalp surface. Random-effects analysis then allows for making inference about the regionally specific effects induced by (potentially) multiple experimental factors in a population.

RESULTS

We illustrate the approach using experimental data acquired during a colour-word matching Stroop task, and show that left frontopolar regions are significantly activated in a population during Stroop effects. This result agrees with previous neuroimaging findings.

COMPARED WITH EXISTING METHODS

The proposed methods (i) address potential misalignment of sensor locations between subjects using spatial interpolation; (ii) produce experimental effects of interest either on a 2D regular grid or on a 3D triangular mesh, both representations of a canonical scalp surface; and (iii) enables one to infer population effects from fNIRS data using a computationally efficient summary statistic approach (random-effects analysis). Significance of regional effects is assessed using random field theory.

CONCLUSIONS

In this paper, we have shown how fNIRS data from multiple subjects can be analysed in sensor space using random-effects analysis.