Zhengchen Cai

Objective: Accurate presurgical evaluation should assess not only the location but also the spatial extent of epileptic activity relative to the surgical cavity, a practical approximation of the epileptogenic zone. We evaluated how electromagnetic source imaging (EMSI) and hemodynamic responses to epileptic discharges relate to the surgical cavity and postoperative outcome, and whether multimodal concordance improves surgical prediction. Methods: Consecutive patients with drug‐resistant focal epilepsy who underwent electroencephalography/magnetoencephalography (EEG/MEG), and EEG/functional magnetic resonance imaging (EEG/fMRI) followed by surgery and ≥1‐year postoperative follow‐up were included. EMSI was performed using maximum entropy on the mean. The most significant fMRI cluster was extracted. All results were projected onto a common cortical surface. Spatial concordance with the surgical cavity was quantified using spatial overlap (area under the receiver operating characteristic curve [AUC]), distance localization error (DLE), and spatial dispersion (SD). Predictive performance was evaluated using a combined spatial criterion (AUC >0.70 and DLE = 0 mm). Results: Twenty‐five patients were included. EEG/MEG, EEG/fMRI, and multimodal analyses were contributive in 23, 14, and 12 patients, respectively. In the EEG/MEG group ( n = 23), patients with good outcome showed higher spatial overlap (mean ± standard deviation, AUC: 0.70 ± 0.14 vs 0.56 ± 0.18; p = 0.04), shorter DLE (4.83 ± 6.80 mm vs 17.98 ± 14.39 mm; p = 0.02), and a trend toward lower SD (24.45 ± 17.07 mm vs 34.21 ± 14.22 mm; p = 0.08) than patients with poor outcome. EEG/fMRI ( n = 14) exhibited similar trends without statistical significance. In predicting surgical outcome, EMSI achieved a negative predictive value of 62% and an accuracy of 61%, whereas EEG/fMRI reached 83% and 86%, respectively. When both modalities were considered ( n = 12), all patients with concordant resected findings achieved a good outcome, whereas 8 of 10 patients with discordant results did not. Significance: EEG/MEG and EEG/fMRI showed complementary profiles. Resection of EEG/MEG localizations were more frequently associated with good outcome, whereas unresected EEG/fMRI responses predicted poor outcome. Concordant and resected multimodal findings were associated with good outcome, supporting a quantitative multimodal approach in presurgical evaluation.

In September 2025, the Organization for Human Brain Mapping (OHBM) organised its first virtual satellite conference in collaboration with the OHBM Sustainability and Environmental Action Special Interest Group (SEA-SIG). This three-day gathering of neuroimagers from around the world was an online complement to the in-person meeting. It had a similar content structure, featuring a half-day educational course, a keynote address from the 2025-2026 OHBM Council Chair Professor Michael Breakspear, three symposia sessions, and an OHBM Special Interest Groups showcase. Here, we summarise the content of this first-of-its-kind event, the benefits for accessibility and sustainability, and provide an outlook for future OHBM accessible and sustainable conference practices.

The hemodynamic response function (HRF) links neuronal activity to functional magnetic resonance imaging (fMRI) signals. While most fMRI studies use a canonical HRF, increasing evidence from studies of healthy subjects suggests that the HRF depends on anatomical location and disease states. Here, we investigate how HRF variability relates to anatomical location and pathology in the epileptic brain, using a large simultaneous electroencephalogram and fMRI dataset. Applying HRF deconvolution and temporal decomposition, we built the first whole-brain HRF library specific to epilepsy, identifying four distinct shape groups. We mapped HRF features across parcellations of two atlases using novel Bayesian hierarchical models. In non-epileptogenic regions, HRF shape and spatial distributions align with findings from healthy subjects. Within pathological regions, they vary significantly according to pathology. Our results indicate that HRF variability is associated with pathology, in addition to its dependence on anatomical location, motivating region- and pathology-based HRF modulation in epilepsy studies.

Excitability is a neuronal property quantified as the magnitude of neural response to stimuli. It plays a crucial role in information processing and is disrupted in various neuropsychiatric conditions. In humans, non-invasive measurements of brain excitability have been mostly limited to the primary motor cortex. Here, the response to Transcranial Magnetic Stimulation (TMS) is quantified as the magnitude of the muscular contraction. TMS mapping of brain excitability outside the motor cortex, simultaneously across brain areas, and in deep regions is challenging. Indeed, TMS has little depth penetration, and can only probe one cortical point at a time. Furthermore, the measurement of the responses to stimuli outside the motor cortex requires simultaneous neuroimaging, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Possible solutions include the application of stimulation approaches alternative to TMS, and the investigation of resting state properties of electromagnetic and hemodynamic brain activity. We show that, in combination with TMS or alone, neuroimaging will progressively allow non-invasive and accurate mapping of excitability with high spatio-temporal resolution, across the entire brain, and non-invasively. Here, we critically discuss the state of the art of whole brain excitability mapping and provide an outlook on neuroscience and clinical implications.

Magnetoencephalography (MEG) is widely used for studying resting-state brain connectivity. However, MEG source imaging is ill posed and has limited spatial resolution. This introduces source-leakage issues, making it challenging to interpret MEG-derived connectivity in resting states. To address this, we validated MEG-derived connectivity from 45 healthy participants using a normative intracranial EEG (iEEG) atlas. The MEG inverse problem was solved using the wavelet-maximum entropy on the mean method. We computed four connectivity metrics: amplitude envelope correlation (AEC), orthogonalized AEC (OAEC), phase locking value (PLV), and weighted-phase lag index (wPLI). We compared spatial correlation between MEG and iEEG connectomes across standard canonical frequency bands. We found moderate spatial correlations between MEG and iEEG connectomes for AEC and PLV. However, when considering metrics that correct/remove zero-lag connectivity (OAEC/wPLI), the spatial correlation between MEG and iEEG connectomes decreased. MEG exhibited higher zero-lag connectivity compared with iEEG. The correlations between MEG and iEEG connectomes suggest that relevant connectivity patterns can be recovered from MEG. However, since these correlations are moderate/low, MEG connectivity results should be interpreted with caution. Metrics that correct for zero-lag connectivity show decreased correlations, highlighting a trade-off; while MEG may capture more connectivity due to source-leakage, removing zero-lag connectivity can eliminate true connections.

The critical importance of justifying the inclusion of covariates is a facet often overlooked in data analysis. While the incorporation of covariates typically follows informal guidelines, we argue for a comprehensive exploration of underlying principles to avoid significant statistical and interpretational challenges. Our focus is on addressing three common yet problematic practices: the indiscriminate lumping of covariates, the lack of rationale for covariate inclusion, and the oversight of potential issues in result reporting. These challenges, prevalent in neuroimaging models involving covariates such as reaction time, demographics, and morphometric measures, can introduce biases, including overestimation, underestimation, masking, sign flipping, or spurious effects. Our exploration of causal inference principles underscores the pivotal role of domain knowledge in guiding covariate selection, challenging the common reliance on statistical measures. This understanding carries implications for experimental design, model-building, and result interpretation. We draw connections between these insights and reproducibility concerns, specifically addressing the selection bias resulting from the widespread practice of strict thresholding, akin to the logical pitfall associated with “double dipping.” Recommendations for robust data analysis involving covariates encompass explicit research question statements, justified covariate inclusions/exclusions, centering quantitative variables for interpretability, appropriate reporting of effect estimates, and advocating a “highlight, don’t hide” approach in result reporting. These suggestions are intended to enhance the robustness, transparency, and reproducibility of covariate-driven analyses, encompassing investigations involving consortium datasets such as ABCD and UK Biobank. We discuss how researchers can use a transparent depiction of the covariate relationships to enhance the ethos of open science and promote research reproducibility.

Significance: We propose NIRSTORM, a software package built within Brainstorm environment, enabling full data analysis of functional Near InfraRed Spectroscopy (fNIRS) data from experiment planning to 3D reconstruction of hemodynamic fluctuations on the cortical surface using optical tomographic approaches. NIRSTORM enables the integration of fNIRS analysis within a multimodal setup making it easy to study fNIRS in combination with other multimodal data such as electroencephalography (EEG) or magnetic resonance imaging (MRI). Aim: NIRSTORM aims to provide an easy-to-use and fully modular toolbox for fNIRS analysis from experimental planning to optical tomography 3D reconstruction extending Brainstorm capacity for multimodal analysis. Approach: NIRSTORM was developed in MATLAB® and integrated as a plugin of the software Brainstorm. Brainstorm is a GUI-oriented, widely used software originally dedicated to statistical analysis and source imaging of EEG and magnetoencephalography (MEG) data. Results: In addition to conventional fNIRS preprocessing steps, including standard channel space and statistical analyses, NIRSTORM provides advanced methods dedicated to optimal probe placement, allowing personalized fNIRS study designs and accurate near-infrared optical tomography within the Maximum Entropy on the Mean (MEM) framework. Conclusion: NIRSTORM is an open-access, user-friendly plugin extending the capacity of Brainstorm, for fNIRS analysis, therefore narrowing the gap between EEG/MEG and hemodynamics.

Electro/Magneto‐EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth‐weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high‐density EEG (HD‐EEG) simulations of epileptic activity and actual MEG/HD‐EEG recordings from patients with focal epilepsy. We incorporated depth‐weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD‐EEG involving a wide range of spatial extents and signal‐to‐noise ratio (SNR) levels, before investigating EMSI on clinical HD‐EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth‐weighted cMEM and depth‐weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth‐weighted cMEM improved the localization when compared to cMEM and depth‐weighted MNE, whereas depth‐weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD‐EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth‐weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD‐EEG and clinical MEG and HD‐EEG for epilepsy patients.

Investigating the relationship between task‐related hemodynamic responses and cortical excitability is challenging because it requires simultaneous measurement of hemodynamic responses while applying noninvasive brain stimulation. Moreover, cortical excitability and task‐related hemodynamic responses are both associated with inter‐/intra‐subject variability. To reliably assess such a relationship, we applied hierarchical Bayesian modeling. This study involved 16 healthy subjects who underwent simultaneous Paired Associative Stimulation (PAS10, PAS25, Sham) while monitoring brain activity using functional Near‐Infrared Spectroscopy (fNIRS), targeting the primary motor cortex (M1). Cortical excitability was measured by Motor Evoked Potentials (MEPs), and the motor task‐related hemodynamic responses were measured using fNIRS 3D reconstructions. We constructed three models to investigate: (1) PAS effects on the M1 excitability, (2) PAS effects on fNIRS hemodynamic responses to a finger tapping task, and (3) the correlation between PAS effects on M1 excitability and PAS effects on task‐related hemodynamic responses. Significant increase in cortical excitability was found following PAS25, whereas a small reduction of the cortical excitability was shown after PAS10 and a subtle increase occurred after sham. Both HbO and HbR absolute amplitudes increased after PAS25 and decreased after PAS10. The probability of the positive correlation between modulation of cortical excitability and hemodynamic activity was 0.77 for HbO and 0.79 for HbR. We demonstrated that PAS stimulation modulates task‐related cortical hemodynamic responses in addition to M1 excitability. Moreover, the positive correlation between PAS modulations of excitability and hemodynamics brought insight into understanding the fundamental properties of cortical function and cortical excitability.