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Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

BACKGROUND Neuronal hyperexcitability characterizes the early stages of Alzheimer’s disease (AD). In animals, early misfolded tau and amyloid-β (Aβ) protein accumulation — both central to AD neuropathology — promote cortical excitability and neuronal network dysfunction. In healthy humans, misfolded tau and Aβ aggregates are first detected, respectively, in the brainstem and frontomedial and temporobasal cortices, decades prior to the onset of AD cognitive symptoms. Whether cortical excitability is related to early brainstem tau — and its associated neuroinflammation — and cortical Aβ aggregations remains unknown. METHODS We probed frontal cortex excitability, using transcranial magnetic stimulation combined with electroencephalography, in a sample of 64 healthy late-middle–aged individuals (50–69 years; 45 women and 19 men). We assessed whole-brain [18F] THK5351 PET uptake as a proxy measure of tau/neuroinflammation, and we assessed whole-brain Aβ burden with [18F] Flutemetamol or [18F] Florbetapir radiotracers. RESULTS We found that higher [18F] THK5351 uptake in a brainstem monoaminergic compartment was associated with increased cortical excitability ( r = 0.29, P = 0.02). By contrast, [18F] THK5351 PET signal in the hippocampal formation, although strongly correlated with brainstem signal in whole-brain voxel-based quantification analyses ( P value corrected for family-wise error [ P FWE-corrected ] < 0.001), was not significantly associated with cortical excitability ( r = 0.14, P = 0.25). Importantly, no significant association was found between early Aβ cortical deposits and cortical excitability ( r = –0.20, P = 0.11). CONCLUSION These findings reveal potential brain substrates for increased cortical excitability in preclinical AD and may constitute functional in vivo correlates of early brainstem tau accumulation and neuroinflammation in humans. TRIAL REGISTRATION EudraCT 2016-001436-35. FUNDING F.R.S.-FNRS Belgium, Wallonie-Bruxelles International, ULiège, Fondation Simone et Pierre Clerdent, European Regional Development Fund.

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Introduction:Quantitative MRI quantifies tissue microstructural properties and supports the characterization of cerebral tissue damages. With an MPM protocol, 4 parameter maps are constructed: MTsat, PD, R1 and R2*, reflecting tissue physical properties associated with iron and myelin contents. Thus, qMRI is a good candidate for in vivo monitoring of cerebral damage and repair mechanisms related to MS. Here, we used qMRI to investigate the longitudinal microstructural changes in MS brain.Methods: Seventeen MS patients (age 25-65, 11 RRMS) were scanned on a 3T MRI, in two sessions separated with a median of 30 months, and the parameters evolution was evaluated within several tissue classes: NAWM, NACGM and NADGM, as well as focal WM lesions. An individual annual rate of change for each qMRI parameter was computed, and its correlation to clinical status was evaluated. For WM plaques, three areas were defined, and a GLMM tested the effect of area, time points, and their interaction on each median qMRI parameter value.Results: Patients with a better clinical evolution, that is, clinically stable or improving state, showed positive annual rate of change in MTsat and R2* within NAWM and NACGM, suggesting repair mechanisms in terms of increased myelin content and/or axonal density as well as edema/inflammation resorption. When examining WM lesions, qMRI parameters within surrounding NAWM showed microstructural modifications, even before any focal lesion is visible on conventional FLAIR MRI. Conclusion:The results illustrate the benefit of multiple qMRI data in monitoring subtle changes within normal appearing brain tissues and plaque dynamics in relation with tissue repair or disease progression.Emilie Lommers and Christophe Phillips equally contributed to the work.