Epileptic activity on foramen ovale electrodes is associated with sleep and tau pathology in Alzheimer's disease.

Both sleep alterations and epileptiform activity are associated with the accumulation of amyloid-ß and tau pathology and are currently investigated for potential therapeutic interventions in Alzheimer's disease (AD). However, a bidirectional intertwining relation between sleep and neuronal hyperexcitability might modulate the effects of AD pathology on the corresponding associations. To investigate this, we performed multiple day simultaneous foramen ovale (FO) plus scalp EEG and polysomnography (PSG) recordings and acquired 18F-MK6240 tau PET-MR in three patients in the prodromal stage of AD and in two patients with mild and moderate dementia due to AD, respectively. As an eligibility criterion for the present study, subjects either had a history of a recent seizure (n = 2) or subclinical epileptiform activity (SEA) on a previous scalp EEG taken in a research context (n = 3). The 18F-MK6240 standard uptake value ratio (SUVR) and asymmetry index (AI) were calculated in a priori defined volumes of interest (VOIs). Linear mixed effects models were used to study associations between interictal epileptiform discharges (IEDs), PSG parameters and 18F-MK6240 SUVR. Epileptiform activity was bilateral but asymmetrically present on FO electrodes in all patients and ≥ 95% of IEDs were not visible on scalp EEG. In one patient two focal seizures were detected on FO electrodes, both without visual scalp EEG correlate. We observed lateralized periodic discharges, brief potentially ictal rhythmic discharges and lateralized rhythmic delta activity on FO electrodes in four patients. Unlike scalp EEG, intracranial electrodes showed a lateralization of epileptiform activity. Although the amount of IEDs on intracranial electrodes was not associated to the 18F-MK6240 SUVR binding in different VOIs, there was a congruent asymmetry of the 18F-MK6240 binding towards the most epileptic hemisphere for the mesial (P = 0.007) and lateral temporal cortex (P = 0.006). IEDs on intracranial electrodes were most abundant during slow wave sleep (SWS) (92/h) and N2 (81/h), followed by N1 (33/h) and least frequent during wakefulness (17/h) and REM sleep (9/h). The extent of IEDs during sleep was not reflected in the relative time in each sleep stage spent (REM% (P = 0.415), N1% (P = 0.668), N2% (P = 0.442), SWS% (P = 0.988)), and not associated with the arousal index (P = 0.317), apnea-hypopnea index (P = 0.846) or oxygen desaturation index (P = 0.746). Together, our observations suggest a multi-directional interaction between sleep, epileptiform activity and tau pathology in AD.

Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence. In this study, we used an uncertainty-centred machine learning pipeline, U-PASS, to automate sleep staging in a challenging real-world dataset of single-channel electroencephalography and accelerometry collected with a wearable device from an elderly population. We were able to effectively limit the uncertainty of our machine learning model and to reliably inform clinical experts of which predictions were uncertain to improve the machine learning model's reliability. This increased the five-stage sleep-scoring accuracy of a state-of-the-art machine learning model from 63.9% to 71.2% on our dataset. Remarkably, the machine learning approach outperformed the human expert in interpreting these wearable data. Manual review by sleep specialists, without specific training for sleep staging on wearable electroencephalography, proved ineffective. The clinical utility of this automated remote monitoring system was also demonstrated, establishing a strong correlation between the predicted sleep parameters and the reference polysomnography parameters, and reproducing known correlations with the apnea-hypopnea index. In essence, this work presents a promising avenue to revolutionize remote patient care through the power of machine learning by the use of an automated data-processing pipeline enhanced with uncertainty estimation.

Subclinical epileptiform activity and sleep disturbances in Alzheimer's disease.

INTRODUCTION: Subclinical epileptiform activity (SEA) and sleep disturbances are frequent in Alzheimer's disease (AD). Both have an important relation to cognition and potential therapeutic implications. We aimed to study a possible relationship between SEA and sleep disturbances in AD. METHODS: In this cross-sectional study, we performed a 24-h ambulatory EEG and polysomnography in 48 AD patients without diagnosis of epilepsy and 34 control subjects. RESULTS: SEA, mainly detected in frontotemporal brain regions during N2 with a median of three spikes/night [IQR1-17], was three times more prevalent in AD. AD patients had lower sleep efficacy, longer wake after sleep onset, more awakenings, more N1%, less REM sleep and a higher apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). Sleep was not different between AD subgroup with SEA (AD-Epi+) and without SEA (AD-Epi-); however, compared to controls, REM% was decreased and AHI and ODI were increased in the AD-Epi+ subgroup. DISCUSSION: Decreased REM sleep and more severe sleep-disordered breathing might be related to SEA in AD. These results could have diagnostic and therapeutic implications and warrant further study at the intersection between sleep and epileptiform activity in AD.

Systematic Review on Fractal Dimension of the Retinal Vasculature in Neurodegeneration and Stroke: Assessment of a Potential Biomarker.

Introduction: Ocular manifestations in several neurological pathologies accentuate the strong relationship between the eye and the brain. Retinal alterations in particular can serve as surrogates for cerebral changes. Offering a "window to the brain," the transparent eye enables non-invasive imaging of these changes in retinal structure and vasculature. Fractal dimension (FD) reflects the overall complexity of the retinal vasculature. Changes in FD could reflect subtle changes in the cerebral vasculature that correspond to preclinical stages of neurodegenerative diseases. In this review, the potential of this retinal vessel metric to serve as a biomarker in neurodegeneration and stroke will be explored. Methods: A literature search was conducted, following the PRISMA Statement 2009 criteria, in four large bibliographic databases (Pubmed, Embase, Web Of Science and Cochrane Library) up to 12 October 2019. Articles have been included based upon their relevance. Wherever possible, level of evidence (LOE) has been assessed by means of the Oxford Centre for Evidence-based Medicine Level of Evidence classification. Results: Twenty-one studies were included for qualitative synthesis. We performed a narrative synthesis and produced summary tables of findings of included papers because methodological heterogeneity precluded a meta-analysis. A significant association was found between decreased FD and neurodegenerative disease, mainly addressing cognitive impairment (CI) and dementia. In acute, subacute as well as chronic settings, decreased FD seems to be associated with stroke. Differences in FD between subtypes of ischemic stroke remain unclear. Conclusions: This review provides a summary of the scientific literature regarding the association between retinal FD and neurodegenerative disease and stroke. Central pathology is associated with a decreased FD, as a measure of microvascular network complexity. As retinal FD reflects the global integrity of the cerebral microvasculature, it is an attractive parameter to explore. Despite obvious concerns, mainly due to a lack of methodological standardization, retinal FD remains a promising non-invasive and low-cost diagnostic biomarker for neurodegenerative and cerebrovascular disease. Before FD can be implemented in clinic as a diagnostic biomarker, the research community should strive for uniformization and standardization.