Longitudinal assessment of oxidative stress markers and their relationship with exposure to PM2.5 and its bound metals in healthy participants.

OBJECTIVE: Exposure to ambient PM2.5 and its bound metals poses a risk to health and disease, via, in part, oxidative stress response. A variety of oxidative stress markers have been used as markers of response, but their relevance to environmental exposure remains to be established. We evaluated, longitudinally, a battery of oxidative stress markers and their relationship with the exposure of PM2.5 and its bound metals in a panel of healthy participants. MATERIAL AND METHODS: Levels of residence- and personal-based ambient air PM2.5 and its bound metals, as well as of lung function parameters, were assessed in a total of 58 questionnaire-administered healthy never smoker participants (male, 39.7%). Levels of urinary oxidative stress markers, including Nε-(hexanoyl)-lysine (HEL; an early lipid peroxidation product), 4-hydroxynonenal (4-HNE), N7-methylguanine (N7-meG), and 8-hydroxy-2-deoxyguanosine (8-OHdG), plasma antioxidants [superoxide dismutase (SOD) and glutathione peroxidase (GPx), and urinary metals were measured by ELISA, LC-MS, and ICP-MS, respectively. The results of three repeated measurements at two-month intervals were analyzed using the Generalized Estimating Equation (GEE). RESULTS: After adjusting for confounders, residence- and personal-based PM2.5 levels were positively associated with HEL (ß = 0.22 and 0.18) and N7-meG (ß = 0.39 and 0.13). Significant correlations were observed between personal air PM2.5-Pb and urinary Pb with HEL (ß = 0.08 and 0.26). While FVC, FEV1, FEV1/FVC, MMF, and PEFR predicted% were normal, a negative interaction (pollutant*time, P < 0.05) was noted for PM2.5-V, Mn, Co, Ni, Zn, As, and Pb. Additionally, a negative interaction was found for N7-meG (ß = -21.35, -18.77, -23.86) and SOD (ß = -26.56, -26.18, -16.48) with FEV1, FVC, and PEFR predicted%, respectively. CONCLUSION: These findings emphasize potential links between environmental exposure, internal dose, and health effects, thereby offering valuable markers for future research on metal exposure, oxidative stress, and health outcomes.

Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature.

Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83-71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments.

Upregulated expression of long non-coding RNA MEG3 serves as a prognostic biomarker in severe pneumonia children and its regulatory mechanism.

Severe pneumonia is a high-mortality disorder in children. The expression and underlying effects of lncRNA maternally expressed 3 (MEG3) were detected. The relationships between MEG3 and other parameters were reported by Pearson correlation. The prognostic importance of MEG3 was assessed by Kaplan-Meier (K-M) curve and COX analysis and its diagnostic potential was uncovered by the receiver operating characteristic (ROC) curve. Luciferase activity assay was performed to demonstrate the target gene of MEG3. Elevated expression of MEG3 and reduced microRNA-29 c (miR-29 c) were evaluated in severe pneumonia children, and a negative relationship between MEG3 and miR-29 c was propounded. MEG3 might function as an independent prognostic indicator. The diagnostic efficiency of MEG3 was also indicated for severe pneumonia children. In MRC-5 cell models and MH-S cell models, lipopolysaccharide (LPS) contributed to the increased expression of MEG3. Interference of MEG3 restricted the upregulation of MEG3 triggered by LPS. Silenced MEG3 protected MRC-5 and MH-S cells against damages managed by LPS on cell apoptosis, viability, and inflammation. MiR-29 c was a ceRNA of MEG3 and the absence of MEG3 abrogated the decreased expression of miR-29 c caused by LPS. Overall, the increased expression of MEG3 and the reduced levels of miR-29 c were identified in severe pneumonia. Prognostic and diagnostic significances of MEG3 provided a novel perspective for severe pneumonia. Disruption of MEG3 alleviated cell injury and inflammation as characterized by high LPS by binding miR-29 c.

Multiple time-point assessment of lncRNA MEG3 shows potential to monitor treatment efficacy in rheumatoid arthritis patients.

Background: This study explored the clinical role of lncRNA MEG3 in rheumatoid arthritis (RA) management. Materials & methods: Totally, 191 active RA patients were enrolled, and their lncRNA MEG3 expressions in peripheral blood monoclonal cells were detected. Results: LncRNA MEG3 expression was downregulated, and it negatively correlated with lesion joints, inflammation and disease activity in RA patients. Moreover, lncRNA MEG3 expression was increased during treatment; meanwhile its increment correlated with treatment response and remission. Conclusion: LncRNA MEG3 may serve as a potential biomarker for monitoring treatment efficacy in RA management.

Whole-head OPM-MEG enables noninvasive assessment of functional connectivity.

A recent paper by Boto et al. established reliability of noninvasive functional connectivity measurements with a new whole-head optically pumped magnetometer magnetoencephalography (OPM-MEG) system. This rapidly developing technology enables a conformal sensor array to accommodate different head sizes and opens up new avenues for experiments in more naturalistic settings.

Statistical power: Implications for planning MEG studies.

Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.

Statistical decision theory and multiscale analyses of human brain data.

BACKGROUND: In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only. NEW METHOD: We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. RESULTS: We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. COMPARISON WITH EXISTING METHODS: This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. CONCLUSIONS: We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.

Objective assessment of automatic language comprehension mechanisms in the brain: Novel E/MEG paradigm.

Assessing the brain activity related to language comprehension is required in a range of situations. Particularly in cases when subjects' cooperation with instructions cannot be guaranteed (e.g., in neurological patients), a protocol is needed that could be independent from attention and behavioral tasks. In this study, we aimed at designing a novel approach for neuromagnetic recordings of brain activity which could allow for probing the neural foundations underpinning three key levels of speech comprehension: lexical, semantic, and (morpho)syntactic, without requiring active attention on speech input or any active task, while keeping the recording session duration as short as possible. To this end, we designed two different auditory paradigms using the same set of single word-based lexical, semantic, and syntactic contrasts: a modified version of the multifeature oddball paradigm and an equiprobable design. Combined magnetoencephalography/electroencephalography data were recorded form young, healthy participants, presented with these stimuli while watching a silent movie. Data from the equiprobable design yielded significant activations in temporal and inferior frontal areas associated with the lexical, semantic, and morphosyntactic contrasts. In turn, neural dissociations observed in the multifeature paradigm emerged mainly in temporal cortices, and were confined to the lexical and semantic conditions with a striking lack of any statistically significant effects for syntactic violations. Our findings indicate that, by employing the equiprobable design, a comprehensive range of key linguistic processes could be assessed in a passive, attention-free manner within a relatively short time (here, ~27 min), thus making this paradigm a time-efficient and patient-friendly tool.

Cost-Effectiveness of Advanced Imaging Technologies in the Presurgical Workup of Epilepsy.

The cost-effectiveness and benefit of many diagnostic tests used in the presurgical evaluation for persons with epilepsy is for the most part uncertain as is their influence on decision-making. The options we have at our disposal are ever increasing. Advanced imaging modalities aim to improve surgical candidacy by helping us better define the epileptogenic zone and optimize surgical planning. However, judicious use is important. Randomized controlled trials delineating which mode of investigation is superior are lacking. Presurgical tests do have incremental value by increasing surgical candidacy and refining surgical planning. The yield of additional imaging will increase with complex localization. However, every case must be tailored by hypothesis, cost, and accessibility. Future studies using a quantitative cost-benefit framework are needed to determine the cost-effectiveness of advanced diagnostic tests (beyond magnetic resonance imaging) in the presurgical evaluation of those with epilepsy.

Sensorimotor Integration and GABA-ergic Activity in Embouchure Dystonia: An Assessment with Magnetoencephalography.

Background: Embouchure dystonia (ED) is a task-specific dystonia affecting musicians thought to be related to alteration in sensorimotor processing and loss of cortical inhibition. Case Report: Magnetoencephalography-coherence source imaging (MEG-CSI) was used to map connectivity between brain regions by imaging neuronal oscillations that are coherent across the brain in patient with ED at rest and while using the index finger to evoke dystonia normally triggered by playing the flute. Discussion: During rest, there was increased coherence in the bilateral frontal and parietal regions that became more focal during dystonia. Diffuse hyperexcitability and increased coherence persisted in bilateral parietal regions as well as the bilateral frontal regions.

The strength of alpha and gamma oscillations predicts behavioral switch costs.

Cognitive flexibility is often examined using task-switch paradigms, whereby individuals either switch between tasks or repeat the same task on successive trials. The behavioral costs of switching in terms of accuracy and reaction time are well-known, but the oscillatory dynamics underlying such costs are poorly understood. Herein, we examined 25 healthy adults who performed a task-switching paradigm during magnetoencephalography (MEG). All MEG data were transformed into the time-frequency domain and significant oscillatory responses were imaged separately per condition (i.e., switch, repeat) using a beamformer. To determine the impact of task-switching on the neural dynamics, the resulting images were examined using paired-samples t-tests. Whole-brain correlations were also computed using the switch-related difference images (switch - repeat) and the switch-related behavioral data (i.e., switch costs). Our key results indicated stronger decreases in alpha and beta activity, and greater increases in gamma activity in nodes of the cingulo-opercular and fronto-parietal networks during switch relative to repeat trials. In addition, behavioral switch costs were positively correlated with switch-related differences in right frontal and inferior parietal alpha activity, and negatively correlated with switch effects in anterior cingulate and right temporoparietal gamma activity. In other words, participants who had a greater decrease in alpha or increase in gamma in these respective regions had smaller behavioral switch costs, which suggests that these oscillations are critical to supporting cognitive flexibility. In sum, we provide novel data linking switch effects and gamma oscillations, and employed a whole-brain approach to directly link switch-related oscillatory differences with switch-related performance differences.

Bayesian multi-dipole modelling in the frequency domain.

BACKGROUND: Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are therefore well suited for source localization in the time and in the frequency domain. In particular, localization of the generators of neural oscillations is very important in the study of cognitive processes in the healthy and in the pathological brain. NEW METHOD: We introduce the use of a Bayesian multi-dipole localization method in the frequency domain. Given the Fourier Transform of the data at one or multiple frequencies and/or trials, the algorithm approximates numerically the posterior distribution with Monte Carlo techniques. RESULTS: We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios and correlated sources. We use dipole clusters to mimic the effect of extended sources. In addition, we test the algorithm on real MEG data to confirm its feasibility. COMPARISON WITH EXISTING METHOD(S): Throughout the whole study, DICS (Dynamic Imaging of Coherent Sources) is used systematically as a benchmark. The two methods provide similar general pictures; the posterior distributions of the Bayesian approach contain much richer information at the price of a higher computational cost. CONCLUSIONS: The Bayesian method described in this paper represents a reliable approach for localization of multiple dipoles in the frequency domain.

Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting.

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation.

Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data.

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each "trial," using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional "states" are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate "trials" from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each 'state' were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.

Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling.

Complex thought and behavior arise through dynamic recruitment of large-scale brain networks. The signatures of this process may be observable in electrophysiological data; yet robust modeling of rapidly changing functional network structure on rapid cognitive timescales remains a considerable challenge. Here, we present one potential solution using Hidden Markov Models (HMMs), which are able to identify brain states characterized by engaging distinct functional networks that reoccur over time. We show how the HMM can be inferred on continuous, parcellated source-space Magnetoencephalography (MEG) task data in an unsupervised manner, without any knowledge of the task timings. We apply this to a freely available MEG dataset in which participants completed a face perception task, and reveal task-dependent HMM states that represent whole-brain dynamic networks transiently bursting at millisecond time scales as cognition unfolds. The analysis pipeline demonstrates a general way in which the HMM can be used to do a statistically valid whole-brain, group-level task analysis on MEG task data, which could be readily adapted to a wide range of task-based studies.

Burden of disease of people with epilepsy during an optimized diagnostic trajectory: costs and quality of life.

BACKGROUND: Diagnosing epilepsy can be lengthy and stressful, potentially leading to increased use of healthcare resources and a reduction in quality of life. AIM: This study aims to determine cost and quality of life before and after an optimized diagnostic procedure for people suspected of having epilepsy from a societal perspective with a follow-up of 12 months. In addition, this study aims to differentiate between people diagnosed with epilepsy during the follow-up of the study and the people who are diagnosed as not having epilepsy or for whom diagnosis is still uncertain. METHODS: A questionnaire regarding the use of healthcare resources was used accompanied by the EQ-5D-3 L. Multiple imputations by chained equations with predictive mean matching was used to account for missing data. To investigate the uncertainty of the results, non-parametric bootstrapped (1000 times) was used. RESULTS: In total, 116 people were included in the study. Total average costs per patient made in the previous 3 months had decreased from €4594 before the optimized diagnostic trajectory to €2609 in the 12 months after the optimized diagnostic trajectory. Healthcare costs were the largest expense group (52-66%) and had decreased significantly from baseline measurement to 12 months after baseline (€2395 vs €1581). Productivity costs had decreased from €1367 to €442 per 3 months. Total annual costs were similar between people diagnosed with epilepsy during the follow-up of the study and the people who are diagnosed as not having epilepsy or for whom diagnosis is still uncertain. Quality of Life had significantly increased over the course of 12 months from 0.80 to 0.84 (Dutch tariff). DISCUSSION: This study indicates that an optimized diagnostic trajectory has positively influenced the use of healthcare resources and the quality of life in people with epilepsy. As chronic care patients make diverse costs, future research should identify the long-term costs after an optimized diagnostic trajectory for patients with epilepsy, possibly identifying patients who are at high risk of becoming high-cost users in the future for early intervention.

Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function.

Recent work has demonstrated that Optically Pumped Magnetometers (OPMs) can be utilised to create a wearable Magnetoencephalography (MEG) system that is motion robust. In this study, we use this system to map eloquent cortex using a clinically validated language lateralisation paradigm (covert verb generation: 120 trials, ∼10 min total duration) in healthy adults (n = 3). We show that it is possible to lateralise and localise language function on a case by case basis using this system. Specifically, we show that at a sensor and source level we can reliably detect a lateralising beta band (15-30 Hz) desynchronization in all subjects. This is the first study of human cognition using OPMs and not only highlights this technology's utility as tool for (developmental) cognitive neuroscience but also its potential to contribute to surgical planning via mapping of eloquent cortex, especially in young children.

Cost-effectiveness of preoperative motor mapping with navigated transcranial magnetic brain stimulation in patients with high-grade glioma.

OBJECTIVE Navigated transcranial magnetic stimulation (nTMS) is used to identify the motor cortex prior to surgery. Yet, there has, until now, been no published evidence on the economic impact of nTMS. This study aims to analyze the cost-effectiveness of nTMS, evaluating the incremental costs of nTMS motor mapping per additional quality-adjusted life year (QALY). By doing so, this study also provides a model allowing for future analysis of general cost-effectiveness of new neuro-oncological treatment options. METHODS The authors used a microsimulation model based on their cohort population sampled for 1000 patients over the time horizon of 2 years. A health care provider perspective was used to assemble direct costs of total treatment. Transition probabilities and health utilities were based on published literature. Effects were stated in QALYs and established for health state subgroups. RESULTS In all scenarios, preoperative mapping was considered cost-effective with a willingness-to-pay threshold < 3*per capita GDP (gross domestic product). The incremental cost-effectiveness ratio (ICER) of nTMS versus no nTMS was 45,086 Euros/QALY. Sensitivity analyses showed robust results with a high impact of total treatment costs and utility of progression-free survival. Comparing the incremental costs caused by nTMS implementation only, the ICER decreased to 1967 Euros/QALY. CONCLUSIONS Motor mapping prior to surgery provides a cost-effective tool to improve the clinical outcome and overall survival of high-grade glioma patients in a resource-limited setting. Moreover, the model used in this study can be used in the future to analyze new treatment options in neuro-oncology in terms of their general cost-effectiveness.

Strategic hospital partnerships: improved access to care and increased epilepsy surgical volume.

OBJECTIVE Surgical treatment of patients with medically refractory focal epilepsy is underutilized. Patients may lack access to surgically proficient centers. The University of California, Irvine (UCI) entered strategic partnerships with 2 epilepsy centers with limited surgical capabilities. A formal memorandum of understanding (MOU) was created to provide epilepsy surgery to patients from these centers. METHODS The authors analyzed UCI surgical and financial data associated with patients undergoing epilepsy surgery between September 2012 and June 2016, before and after institution of the MOU. Variables collected included the length of stay, patient age, seizure semiology, use of invasive monitoring, and site of surgery as well as the monthly number of single-surgery cases, complex cases (i.e., staged surgeries), and overall number of surgery cases. RESULTS Over the 46 months of the study, a total of 104 patients underwent a total of 200 operations; 71 operations were performed in 39 patients during the pre-MOU period (28 months) and 129 operations were performed in 200 patients during the post-MOU period (18 months). There was a significant difference in the use of invasive monitoring, the site of surgery, the final therapy, and the type of insurance. The number of single-surgery cases, complex-surgery cases, and the overall number of cases increased significantly. CONCLUSIONS Partnerships with outside epilepsy centers are a means to increase access to surgical care. These partnerships are likely reproducible, can be mutually beneficial to all centers involved, and ultimately improve patient access to care.

EEG/MEG source imaging using fMRI informed time-variant constraints.

Multimodal functional neuroimaging by combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) or magnetoencephalography (MEG) is able to provide high spatiotemporal resolution mapping of brain activity. However, the accuracy of fMRI-constrained EEG/MEG source imaging may be degraded by potential spatial mismatches between the locations of fMRI activation and electrical source activities. To address this problem, we propose a novel fMRI informed time-variant constraint (FITC) method. The weights in FITC are determined by combining the fMRI activities and electrical source activities in a time-variant manner to reduce the impact of the fMRI extra sources. The fMRI weights are modified using cross-talk matrix and normalized partial area under the curve to reduce the impact of fMRI missing sources. Monte Carlo simulations were performed to compare the source estimates produced by L2-minimum norm estimation (MNE), fMRI-weighted minimum norm estimation (fMNE), FITC, and depth-weighted FITC (wFITC) algorithms with various spatial mismatch conditions. Localization error and temporal correlation were calculated to compare the four algorithms under different conditions. The simulation results indicated that the FITC and wFITC methods were more robust than the MNE and fMNE algorithms. Moreover, FITC and wFITC were significantly better than fMNE under the fMRI missing sources condition. A human visual-stimulus EEG, MEG, and fMRI test was performed, and the experimental data revealed that FITC and wFITC displayed more focal areas than fMNE and MNE. In conclusion, the proposed FITC method is able to better resolve the spatial mismatch problems encountered in fMRI-constrained EEG/MEG source imaging.