Edouard Delaire

Recent years have seen a rise in investigations on the organizational principles of brain networks in healthy and pathological conditions. Temporal lobe epilepsy (TLE) is a compelling case as its focal pathology is accompanied by widespread network disruptions. However, how TLE affects functional connector hubs, regions supporting long-range and inter-network communication, remain poorly understood. We mapped connector hub reorganization in patients with TLE relative to healthy controls. Patients exhibited widespread connector hub reductions in the ipsilateral occipital, cingulate, precuneus and hippocampal regions as well as the bilateral pallidum. Hub reductions occurred alongside the emergence of new hubs in contralateral regions. We furthermore observed strong associations between hub reductions and microstructural alterations in subcortical structures and the hippocampus. These findings demonstrate widespread, asymmetric connector hub reorganization extending beyond the epileptogenic temporal lobe, revealing distinct patterns of network disruption and brain-wide reorganization.

Functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) both rely on the phenomenon of neurovascular coupling (NVC) to probe brain activity through their sensitivity to cerebral blood oxygenation. However, the relationship between fNIRS chromophores (oxy- and deoxyhaemoglobin, HbO and HbR), and fMRI (Blood Oxygen Level Dependent and Arterial Spin Labeling, BOLD and ASL) measurements, and whether this relationship remains consistent across subjects and physiological conditions, has only been partially characterised. We acquired concurrent continuous-wave fNIRS and gradient-echo (GE) and spin-echo (SE) BOLD-ASL fMRI in healthy adults (n = 10) during visual stimulation. By applying calibrated fMRI methodology, we examined the relationships between fNIRS-derived haemoglobin modulations and fMRI-derived modulations in macrovascular (GE-) and microvascular (SE-) BOLD signals, cerebral blood flow (CBF), and oxygen metabolism (CMRO₂). Group-level results showed strong temporal cross-modal agreement, with HbO and HbR tightly mirroring all fMRI signal time-courses (|r| > 0.8). A quantitative analysis of trial-by-trial modulations revealed distinct state-dependent behaviours: HbO maintained a stable relationship with the fMRI-derived metrics across conditions, whereas cross-modal relationships between HbR and fMRI-derived metrics substantially strengthened at higher flow-metabolism coupling (FMC), the ratio of CBF to CMRO₂ change, an index of the strength of NVC. Both HbO and HbR were more strongly associated with GE-BOLD than with SE-BOLD. These findings provide a rigorous physiological grounding for fNIRS signal interpretation, demonstrating its utility as a surrogate marker for specific haemodynamic and metabolic parameters.

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.

Although rapid eye movement (REM) sleep is often thought of as a singular state, it consists of two substates, phasic and tonic REM, defined by the presence (respectively absence) of bursts of rapid eye movements. These two substates have distinct EEG signatures and functional properties. However, whether they exhibit regional specificities remains unknown. Using intracranial EEG recordings from 31 patients, we analyzed expert-labeled segments from tonic and phasic REM and contrasted them with wakefulness segments. We assessed the spectral and connectivity content of these segments using Welch’s method to estimate power spectral density and the phase locking value to assess functional connectivity. Overall, we found a widespread power gradient between low and high frequencies (p < 0.05, Cohen’s d = 0.17 ± 0.20), with tonic REM being dominated by lower frequencies (p < 0.01, d = 0.18 ± 0.08), and phasic REM by higher frequencies (p < 0.01, d = 0.18 ± 0.19). However, some regions, such as the occipito-temporal areas as well as medial frontal regions, exhibit opposite trends. Connectivity was overall higher in all bands except in the low and high ripple frequency bands in most networks during tonic REM (p < 0.01, d = 0.08 ± 0.09) compared to phasic REM. Yet, functional connections involving the visual network were always stronger during phasic REM when compared to tonic REM. These findings highlight the spatiotemporal heterogeneity of REM sleep which is consistent with the concept of focal sleep in humans.

Functional near-infrared spectroscopy (fNIRS) is a wearable, noninvasive imaging technique able to monitor brain hemodynamic activity^{1 – 4} using optical sensors (or optodes) attached to the head of the subject. fNIRS consists of installing a set of optodes (light-emitting sources and light detectors) on the scalp of a participant. Light sources emit near-infrared light using two or more wavelengths typically in the range of 650 to 900 nm. From the light intensity measured on fNIRS detectors, one can then estimate with good temporal accuracy the local variations in oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT = HbO + HbR) concentrations between a source and a detector (also called a fNIRS “channel”) using at least two wavelengths.⁴ A standard fNIRS experiment can be divided into three steps, as described in further detail in Yücel et al.⁵: acquisition planning, data acquisition, and data analysis.

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.

Functional near-infrared spectroscopy (fNIRS) is a family of noninvasive neuroimaging techniques that estimate concentrations of oxygenated and deoxygenated hemoglobin near the surface of the brain and thus infer relative changes in neural activation.¹ There are various technical approaches to NIRS measurements, most prominently the continuous-wave, frequency domain, and time-domain techniques. Each approach provides unique benefits, utilizes different instrumentation, and yields different raw data.^1,2