Mélanie Pélégrini-Issac

The diagnosis of amyotrophic lateral sclerosis requires identifying degeneration in both brain and bulbospinal motor neurons. However, detecting cortical dysfunction remains challenging, as peripheral symptoms often overshadow upper motor neuron signs. Although transcranial magnetic stimulation and MRI are valuable tools, transcranial magnetic stimulation is challenged as disease progresses but also at early stage in some patients, and brain MRI shows in most cohorts no significant change at the time of diagnosis. This emphasizes the need for neuromarkers facilitating detection of cortical dysfunction and longitudinal monitoring. EEG offers promising avenues. Accordingly, we recently identified altered theta-gamma phase–amplitude coupling in amyotrophic lateral sclerosis. The present study aimed to further explore phase–amplitude coupling in patients, focusing not only on theta and gamma bands but also on alpha and beta bands, and the link with handedness and brain structure. Resting-state EEG was recorded in 26 patients with amyotrophic lateral sclerosis and 26 age- and sex-matched controls, alongside anatomical and diffusion MRI. PAC was calculated between slow and gamma oscillations at five sensorimotor electrodes bilaterally. Grey and white matter integrity was evaluated through cortical thickness measurements and diffusion metrics along the corticospinal tract. Results revealed significantly decreased theta-gamma PAC in the dominant hemisphere of patients, without changes in band powers or other frequency couplings. MRI confirmed well-known handedness-related brain structural asymmetry in both groups, although it was less pronounced in patients. Specifically, diffusion metrics were altered in the most caudal segment (brainstem level) of the pyramidal tract within the dominant hemisphere in patients. These findings align with lateralized theta-gamma PAC alterations and the greater vulnerability of the dominant hemisphere to amyotrophic lateral sclerosis. No correlation was found between electrophysiological and diffusion metrics, likely because they are related to different mechanisms: PAC alteration being presumably linked to excitation/inhibition imbalance preceding upper motor neuron degeneration. Moreover, theta–gamma PAC was found to be particularly altered in patients with altered cognitive scores, consistent with previous findings in patients with mild cognitive impairment. Lastly, receiver operating characteristic analyses demonstrated that PAC outperformed diffusion MRI in diagnostic accuracy, underscoring its potential as a very sensitive marker of cortical dysfunction in amyotrophic lateral sclerosis. Although these results need validation in a larger cohort at different stages of the disease and across different forms (sporadic and familial), they confirm that PAC can detect cortical dysfunctions in amyotrophic lateral sclerosis.

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.

Moderate to severe traumatic brain injury (TBI) should be considered as a chronic health condition. The corpus callosum is the brain region that suffers most from diffuse axonal injury, leading to long-term functional deficits. Few studies have considered the relationships between inter- and intrahemispheric functional connectivity and structural damages to the corpus callosum in chronic TBI patients. We examined how callosal functional connectivity and white matter alterations relate to clinical outcome using multimodal magnetic resonance imaging (MRI): structural MRI estimates callosal volume, diffusion-weighted MRI enables white matter integrity quantification, resting-state functional MRI assesses neural dysfunction. Seventy-four patients underwent a multimodal MRI session on average 5 years after a moderate-to-severe TBI. Multiple factorial analysis analyzed the relationships between clinical outcome (from severe disability to good recovery, assessed by the Glasgow Outcome Scale extended GOSE), callosal volume, diffusion metrics (fractional anisotropy and mean, axial, and radial diffusivity), and inter- and intrahemispheric functional connectivity. Multiple factorial analysis confirmed that patients with severe disability (GOSE 3-4) had more structural alterations in the corpus callosum than patients with a good recovery (GOSE 7-8). Most importantly, patients able to live independently but unable to work/study in a standard environment (GOSE 5-6) could not be described solely by structural features. They exhibited a lower interhemispheric connectivity between cortical regions mediated by the corpus callosum than patients with a good recovery, and a tendency towards a decrease in intrahemispheric connectivity compared with severely disabled patients. These findings suggest a complex long-term functional impact of moderate-to-severe TBI.

Background and Objectives. GRN variants are a frequent cause of familial frontotemporal dementia (FTD). Monitoring disease progression in asymptomatic carriers of genetic variants is a major challenge in delivering preventative therapies before clinical onset. This study aimed to assess the usefulness of fluorodeoxyglucose (FDG)-PET in identifying metabolic changes in presymptomatic GRN carriers (PS-GRN+), and to trace their longitudinal progression. Methods. Participants were longitudinally evaluated over 5 years in a prospective cohort study focused on GRN disease (Predict-PGRN). They underwent cognitive/behavioral assessment, plasma neurofilament measurement, brain MRI and FDG-PET. Voxel-wise comparisons of structural and metabolic imaging data between the two groups were performed for each time-point. Longitudinal PET changes were evaluated with voxel-wise comparisons and the metabolic percent annual changes method. The association of regional brain metabolism with plasma neurofilament and cognitive changes was analyzed. Results. Among the 80 individuals enrolled in the study, 58 (27 PS-GRN+ and 31 non-carriers) were included in the analyses. Cross-sectional comparisons between PS-GRN+ and controls found a significant hypometabolism in the left superior temporal sulcus (STS) region (encompassing the middle and superior temporal gyri), approximately 15 years before the expected disease onset, without significant cortical atrophy. The longitudinal metabolic decline over the following 5 years peaked around the right STS in carriers (pp=0.004). Lower glucose uptake was associated with higher neurofilament increase (p=0.003) and lower frontal cognitive scores (p=0.014) in PS-GRN+. Discussion. This study detected brain metabolic changes in the STS region, preceding structural and cognitive alterations, thus contributing to the characterization of the pathochronology of preclinical GRN disease. Due to the STS involvement in the perception of facially communicated cues, it is likely that its dysfunction contributes to social cognition deficits characterizing FTD. Overall, our study highlights brain metabolic changes as an early disease-tracking biomarker, and proposes annualized percent decrease as a metric to monitor therapeutic response in forthcoming trials.

Purpose A reliable tool for outcome prognostication in severe traumatic brain injury (TBI) would improve intensive care unit (ICU) decision-making process by providing objective information to caregivers and family. This study aimed at designing a new classification score based on magnetic resonance (MR) diffusion metrics measured in the deep white matter between day 7 and day 35 after TBI to predict 1-year clinical outcome. Methods Two multicenter cohorts (29 centers) were used. MRI-COMA cohort (NCT00577954) was split into MRI-COMA-Train (50 patients enrolled between 2006 and mid-2014) and MRI-COMA-Test (140 patients followed up in clinical routine from 2014) sub-cohorts. These latter patients were pooled with 56 ICU patients (enrolled from 2014 to 2020) from CENTER-TBI cohort (NCT02210221). Patients were dichotomised depending on their 1-year Glasgow outcome scale extended (GOSE) score: GOSE 1–3, unfavorable outcome (UFO); GOSE 4–8, favorable outcome (FO). A support vector classifier incorporating fractional anisotropy and mean diffusivity measured in deep white matter, and age at the time of injury was developed to predict whether the patients would be either UFO or FO. Results The model achieved an area under the ROC curve of 0.93 on MRI-COMA-Train training dataset, and 49% sensitivity for 96.8% specificity in predicting UFO and 58.5% sensitivity for 97.1% specificity in predicting FO on the pooled MRI-COMA-Test and CENTER-TBI validation datasets. Conclusion The model successfully identified, with a specificity compatible with a personalized decision-making process in ICU, one in two patients who had an unfavorable outcome at 1 year after the injury, and two-thirds of the patients who experienced a favorable outcome.

The human gait program involves many brain areas such as motor cortices, cerebellum, basal ganglia, brainstem, and spinal cord. The mesencephalic locomotor region (MLR), which contains the pedunculopontine (PPN) and cuneiform (CN) nuclei, is thought to be one of the key supraspinal gait generators. In daily life activities, gait primarily occurs in complex conditions, such as through narrow spaces, or while changing direction or performing motor or cognitive tasks. Here, we aim to explore the activity of these subcortical brain areas while walking through narrow spaces, using functional MRI in healthy volunteers and designing a virtual reality task mimicking walking down a hallway, without and with an open doorway to walk through. As a control, we used a virtual moving walkway in the same environment. Twenty healthy volunteers were scanned. Fifteen subjects were selected for second level analysis based on their ability to activate motor cortices. Using the contrast Gait versus Walkway, we found activated clusters in motor cortices, cerebellum, red nucleus, thalamus, and the left MLR including the CN and PPN. Using the contrast Gait with Doorway versus Walkway with Doorway, we found activated clusters in motor cortices, left putamen, left internal pallidum, left substantia nigra, right subthalamic area, and bilateral MLR involving the CN and PPN. Our results suggest that unobstructed gait involves a motor network including the PPN whereas gait through a narrow space requires the additional participation of basal ganglia and bilateral MLR, which may encode environmental cues to adapt locomotion.