Biology subjects

Gotman, J.

Publications and source records attributed to Gotman, J..

4 recordsLinked to original sources

bioRxiv2026

HIPPOCAMPAL TAU RELATED SIGNAL INDEXES HYPEREXCITABILITY AND NREM SLEEP MEMORY DYSFUNCTION IN TEMPORAL LOBE EPILEPSY

Background Sleep and memory disturbances are common in temporal lobe epilepsy (TLE), yet their relationship with tau-targeted neuroimaging measures remains unclear. We investigated whether in-vivo temporal 18F-MK6240 tracer retention relates to seizure burden, non-rapid eye movement (NREM) sleep microstructure, and memory performance in TLE. Methods This cross-sectional study was conducted from 2019 to 2024 at the Montreal Neurological Institute-Hospital. Eligible patients had unilateral TLE diagnosed according to International League Against Epilepsy criteria and underwent overnight electroencephalography (EEG) recordings, research-protocol 3T magnetic resonance imaging, and 18F-MK6240 positron emission tomography. Regional 18F-MK6240 standardized uptake value ratios (SUVRs), using the cerebellum as the reference region, were quantified in the ipsilateral hippocampus (defined a priori as the primary region of interest), as well as in peri-hippocampal and lateral temporal regions. Electroclinical and neuropsychological measures, including seizure burden (high vs low), N2 interictal epileptiform discharge frequency (frequent vs non-frequent), and memory performance (impaired vs intact), were classified according to predefined clinical criteria. N2 spindle rate and N3 slow-wave rate were quantified from overnight EEG recordings. Findings Of 70 eligible patients, 25 were included (eight [32%] females; mean age 35.2 years [SD 12.1]). Higher ipsilateral hippocampal 18F-MK6240 uptake was observed in participants with greater focal seizure burden (median [IQR] 0.70 [0.67-0.78] vs 0.64 [0.58-0.65], rank-biserial correlation=-0.58, 95% CI -0.82 to -0.19; p=0.02), was associated with lower N2 spindle rate (r=-0.42, 95% CI -0.70 to -0.03; p=0.04) and higher N3 slow-wave rate (r=0.47; 95% CI 0.09 to 0.73; p=0.02), and was also higher in participants with impaired than intact memory performance (n=21; mean [SD] 0.80 [0.12] vs 0.68 [0.10], Cohen's d=1.15, 95% CI 0.07 to 2.21; p=0.04). Associations outside the hippocampus were generally less consistent and less precise, with the largest non-hippocampal estimate observed in peri-hippocampal regions. Interpretation Higher hippocampal 18F-MK6240 uptake was associated with greater focal seizure burden, altered NREM sleep microstructure, and impaired memory performance in TLE. These findings suggest that hippocampal 18F-MK6240 uptake may represent a candidate marker of circuit vulnerability associated with NREM sleep-memory dysfunction in TLE, warranting validation in larger longitudinal studies.

neuroscience↗

bioRxiv2025

The Hemodynamic Response Function Varies Across Anatomical Location and Pathology in the Epileptic Brain

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.

neuroscience↗

bioRxiv2022

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

BackgroundMagnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. MethodWe validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. ResultsThe MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. ConclusionThis study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies. HighlightsO_LIValidation of MEG source imaging with intracranial EEE atlas C_LIO_LIAssessment of resting state human brain oscillations from healthy brain C_LIO_LIAdapted source imaging method, wMEM, to localize resting state oscillations C_LIO_LIIdentified brain regions with oscillations accurately estimated by MEG C_LIO_LIMEG estimated spectra dominated by oscillations in the alpha band C_LI

neuroscience↗

bioRxiv2018

Identification negative BOLD responses using windkessel models

Alongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF. Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.

neuroscience↗