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Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.

Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.

We present Clinica ( www.clinica.run ), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Resting-state functional magnetic resonance imaging (rsfMRI) allows the study of functional brain connectivity based on spatially structured variations in neuronal activity. Proper evaluation of connectivity requires removal of non-neural contributions to the fMRI signal, in particular hemodynamic changes associated with autonomic variability. Regression analysis based on autonomic indicator signals has been used for this purpose, but may be inadequate if neuronal and autonomic activities covary. To investigate this potential co-variation, we performed rsfMRI experiments while concurrently acquiring electroencephalography (EEG) and autonomic indicator signals, including heart rate, respiratory depth, and peripheral vascular tone. We identified a recurrent and systematic spatiotemporal pattern of fMRI (named as fMRI cascade), which features brief signal reductions in salience and default-mode networks and the thalamus, followed by a biphasic global change with a sensory-motor dominance. This fMRI cascade, which was mostly observed during eyes-closed condition, was accompanied by large EEG and autonomic changes indicative of arousal modulations. Importantly, the removal of the fMRI cascade dynamics from rsfMRI diminished its correlations with various signals. These results suggest that the rsfMRI correlations with various physiological and neural signals are not independent but arise, at least partly, from the fMRI cascades and associated neural and physiological changes at arousal modulations.

White matter hyperintensities (WMH) are common in older adults and are associated with cognitive disorders. They typically arise from small vessel disease, leading to demyelination and axonal loss. WMH are thus considered markers of cerebrovascular changes. However, other pathophysiological processes can lead to WMH, particularly in Alzheimer’s disease (AD). Understanding the diverse origins of WMH could enhance the diagnosis and treatment of AD patients. We hypothesize that multimodal neuroimaging could help understand the heterogeneity of WMH and pinpoint their specific origin. We included 142 older adults from the community and memory clinic (with an emphasis on patients within the Alzheimer’s continuum), and tested if multimodal neuroimaging signal within regional WMH (including T1w, T2w, 18 F-florbetapir [AV45] and 18 F-fluorodeoxyglucose [FDG] PET), is associated with amyloid load and cognition. We showed that intra-WMH T1w and T2w signal in the parietal and frontal lobes were linked to amyloid status; intra-WMH T2w signal in all regions negatively correlated with amyloid load, while intra-WMH T1w signals in the parietal lobe positively correlated with amyloid load; finally, intra-WMH T1w signal negatively correlated with cognition while T2w and marginally AV45 signals positively correlated with cognition. This study demonstrates the potential of multimodal neuroimaging to unravel the heterogeneity of WMH, which could enhance their interpretation and improve clinical decision-making.

Atypical social impairments (i.e., impaired social cognition and social communication) are vital manifestations of autism spectrum disorder (ASD) patients, and the incidence rate of ASD is significantly higher in males than in females. Characterizing the atypical brain patterns underlying social deficits of ASD is significant for understanding the pathogenesis. However, there are no robust imaging biomarkers that are specific to ASD, which may be due to neurobiological complexity and limitations of single‐modality research. To describe the multimodal brain patterns related to social deficits in ASD, we highlighted the potential functional role of white matter (WM) and incorporated WM functional activity and gray matter structure into multimodal fusion. Gray matter volume (GMV) and fractional amplitude of low‐frequency fluctuations of WM (WM‐fALFF) were combined by fusion analysis model adopting the social behavior. Our results revealed multimodal spatial patterns associated with Social Responsiveness Scale multiple scores in ASD. Specifically, GMV exhibited a consistent brain pattern, in which salience network and limbic system were commonly identified associated with all multiple social impairments. More divergent brain patterns in WM‐fALFF were explored, suggesting that WM functional activity is more sensitive to ASD's complex social impairments. Moreover, brain regions related to social impairment may be potentially interconnected across modalities. Cross‐site validation established the repeatability of our results. Our research findings contribute to understanding the neural mechanisms underlying social disorders in ASD and affirm the feasibility of identifying biomarkers from functional activity in WM. This work revealed the multimodal brain patterns (the salience network and limbic system) to explain the mechanism of autism spectrum disorder social impairment in gray matter (GM) structure and white matter (WM) function information, and WM functional activity was more sensitive to multiple social impairments than GM.

The concept of cognitive reserve (CR) originated from discrepancies between the degree of brain pathology and the severity of clinical manifestations. CR has been characterized through CR proxies, such as education and occupation complexity; however, such approaches have inherent limitations. Although several methods have been developed to overcome these limitations, they fail to reflect the entire Alzheimer's disease (AD) pathology. Meanwhile, graph theory analysis, one of most powerful and flexible approaches, have established remarkable network properties of the brain. The functional and structural brain networks are damaged in neurodegenerative diseases. Therefore, network analysis has been applied to clarify the characteristics of the disease or give insight. Here, using multimodal neuroimaging, we propose an intuitive model to estimate CR based on its original definition, and explore the neural substrates of CR from the perspective of networks and functional connectivity. A total of 87 subjects (21 AD, 32 mild cognitive impairment, and 34 normal aging) underwent tau and amyloid PET, 3D T1-weighted MR, and resting-state fMRI. We hypothesized CR as a residual of actual cognitive performance and expected performance to be related to quantitative factors, such as AD pathology, demographics, and a genetic factor. Then, we correlated this marker using education and occupation complexity as conventional CR proxies. We validated this marker by testing whether it would modulate the effect of brain pathology on memory function. To examine the neural substrates associated with CR, we performed graph analysis to investigate the association between the CR marker and network measures at different granularities in total subjects, AD spectrum and normal aging, respectively. The CR marker from our model was well associated with education and occupation complexity. More directly, the CR marker was revealed to modify the relationship between brain pathology and memory function among AD spectrum. The CR marker was correlated with the global efficiency of the entire network, nodal clustering coefficient, and local efficiency of the right middle-temporal pole. In connectivity analysis, one cluster of edges centered on right middle-temporal pole was significantly correlated with the CR marker. In subgroup analysis, the network measures of right middle-temporal pole still correlated with the CR marker among AD spectrum. However, right precentral gyrus was revealed to be associated with the CR marker in normal aging. This study demonstrates that our intuitive model using multimodal neuroimaging and network perspective adequately and comprehensively captures CR. From a network perspective, CR is associated with the capacity to process information efficiently in the brain. The right middle-temporal pole was revealed to be a pivotal neural substrate of CR in AD spectrum. These findings foster understanding of AD and will be useful to help identify individuals with vulnerability or resistance to AD pathology, and characterize patients for intervention or drug trials. •We generated an equation to quantify cognitive reserve (CR) reflecting overall AD neuropathology using multimodal neuroimaging techniques.•CR was proved to modify the relationship between brain pathology and memory function in AD spectrum.•CR was associated with global network efficiency and nodal clustering coefficient and local efficiency of the right middle-temporal pole.•Graph parameters of right middle-temporal pole was revealed to modulate the effect of brain atrophy on cognition in AD spectrum.

•Previous research failed to show a clear hierarchical structure in the time-domain variability of the brain's structure-function coupling.•We show that the time-domain variability of structure-function coupling is higher in transmodal than unimodal cortices.•Time-domain variability of coupling shows diverging relationships with myelination and synaptic density.•Long-range vs. short-range fiber tracts explain coupling patterns across the cortex.•A lower and more variable alignment between structure and function favors the emergence of unique functional dynamics. The relationship between the brain's structural wiring and its dynamic activity is thought to vary regionally, implying that the mechanisms underlying structure-function coupling may differ depending on a region's position within the brain's hierarchy. To better bridge the gap between structure and function, it is crucial to identify the factors shaping this regionality, not only in terms of how static functional connectivity aligns with structure, but also regarding the time-domain variability of this interplay. Here we map structure - function coupling and its time-domain variability and relate them to the heterogeneity of the cortex. We show that these two properties split the cortical landscape into two districts anchored to the opposite ends of the brain's hierarchy. By looking at statistical relationships with layer-specific gene transcription, T1w/T2 w ratio, and synaptic density, we show that macro-scale structure-function coupling may be rooted in the brain's microstructure and meso‑scale laminar specialization. Finally, we demonstrate that a lower and more variable alignment of function and structure may bestow the emergence of unique functional dynamics.

Simultaneous measurement of electrophysiological and hemodynamic brain signals imposes special requirements on the instrumentation. Here, we developed a high‐density fiberoptic probe for concurrent diffuse optical tomography (DOT) and magnetoencephalography (MEG) recordings. Transparent two‐component silicone was mixed with carbon black dye to achieve a black, flexible, non‐magnetic support for the dense optode arrangement and low (5 mm) probe thickness. The probe was used to record somatosensory responses to electrical right median nerve stimulation at 0.5, 1, 2, and 4 Hz in 18 adult human subjects. Brain activity was simultaneously measured with a commercial whole‐head MEG system and with the DOT optode arrangement covering approximately 40 cm2 over the parietal region in the contralateral left hemisphere. Two correlation‐based clustering methods were developed to find regions where the reconstructed time course of total hemoglobin concentration (HbT) changes correlated with the predicted hemodynamic activity based on time‐course characteristics of the MEG sources and the canonical hemodynamic response model. Two statistically significant clusters were found based on the correlation between HbT around the postcentral gyrus and MEG primary somatosensory cortical activity at ~35 ms (P35m response). In addition, correlation between HbT and secondary somatosensory cortical activity suggested a statistically significant cluster in the postcentral gyrus and parietal operculum. These results illustrate an improvement in localization over previous DOT studies using sparse optode arrangements, and demonstrate the feasibility of the system for simultaneous HD‐DOT‐MEG experiments. Furthermore, the techniques described here pave the way for understanding the coupling between hemodynamic and electrophysiological responses. Further research is needed to reveal the neuronal circuits giving rise to the correlating MEG and DOT response features. Significant improvements in the technology are still expected via optimization of the detected light power in the instrumentation. Fiberoptic probe for simultaneous high‐density diffuse optical tomography (HD‐DOT) and magnetoencephalography (MEG) was constructed and tested in a somatosensory experiment. Equivalent current dipoles were fitted to the MEG data. SI and SII dipole waveform features were extracted and used to localize correlated hemodynamic activity in the postcentral gyrus.

•Multimodal neuroimaging links dmPFC to delayed discounting.•Stimulation of dmPFC reduces delayed discounting.•Stimulation of dmPFC decreases the congruency effect in the egocentric perspective.•The dmPFC promotes self-control by inhibiting egocentric perspective. The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate—the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.