Altered EEG power spectrum, but not sleep-wake architecture, in HCN1 knockout mice

Bleakley, Lauren E; Keenan, Ryan J.; Graven, Rachel D.; Metha, Jeremy A.; Ma, Sherie; Daykin, Heather; Cornthwaite-Duncan, Linda; Hoyer, Daniel; Reid, C.; Jacobson, Laura

doi:10.1016/j.bbr.2022.114105

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…The use of stem cell and organoid models, and generation of transgenic mice, will advance understanding of disease mechanisms, and provide models upon which to develop targeted therapies, as done recently for HCN1 . 50…”

Section: Discussionmentioning

confidence: 99%

Mono and biallelic variants inHCN2cause severe neurodevelopmental disorders

Houdayer,

Phillips,

Chabbert

et al. 2024

Preprint

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Hyperpolarization activated Cyclic Nucleotide (HCN) gated channels are crucial for various neurophysiological functions, including learning and sensory functions, and their dysfunction are responsible for brain disorders, such as epilepsy. To date,HCN2variants have only been associated with mild epilepsy and recently, one monoallelic missense variant has been linked to developmental and epileptic encephalopathy. Here, we expand the phenotypic spectrum ofHCN2-related disorders by describing twenty-one additional individuals from fifteen unrelated families carrying <HCN2> variants. Seventeen individuals had developmental delay/intellectual disability (DD/ID), two had borderline DD/ID, and one had borderline DD. Ten individuals had epilepsy with DD/ID, with median age of onset of 10 months, and one had epilepsy with normal development. Molecular diagnosis identified thirteen different pathogenicHCN2variants, including eleven missense variants affecting highly conserved amino acids, one frameshift variant, and one in-frame deletion. Seven variants were monoallelic of which five occurred de novo, one was not maternally inherited, one was inherited from a father with mild learning disabilities, and one was of unknown inheritance. The remaining six variants were biallelic, with four homozygous and two compound heterozygous variants. Functional studies using two-electrode voltage-clamp recordings in <Xenopus laevis> oocytes were performed on three monoallelic variants, p.(Arg324His), p.(Ala363Val), and p.(Met374Leu), and three biallelic variants, p.(Leu377His), p.(Pro493Leu) and p.(Gly587Asp). The p.(Arg324His) variant induced a strong increase of HCN2 conductance, while p.(Ala363Val) and p.(Met374Leu) displayed dominant negative effects, leading to a partial loss of HCN2 channel function. By confocal imaging, we found that the p.(Leu377His), p.(Pro493Leu) and p.(Gly587Asp) pathogenic variants impaired membrane trafficking, resulting in a complete loss of HCN2 elicited currents in <Xenopus> oocytes. Structural 3D-analysis in depolarized and hyperpolarized states of HCN2 channels, revealed that the pathogenic variants p.(His205Gln), p.(Ser409Leu), p.(Arg324Cys), p.(Asn369Ser) and p.(Gly460Asp) modify molecular interactions altering HCN2 function. Taken together, our data broadens the clinical spectrum associated withHCN2variants, and disclose thatHCN2is involved in developmental encephalopathy with or without epilepsy.

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Section: Discussionmentioning

confidence: 99%

Mono and biallelic variants inHCN2cause severe neurodevelopmental disorders

Houdayer,

Phillips,

Chabbert

et al. 2024

Preprint

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A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals

Ehiabhi

Wang

2023

BioMedInformatics

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With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health analysis as well as the application of machine-learning techniques for analyzing multi-variate/multi-channel multi-modal biometric signals.This study reviewed the predominant mental-health-related biosensors, including polysomnography (PSG), electroencephalogram (EEG), electro-oculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). We also described the processes used for data acquisition, data-cleaning, feature extraction, machine-learning modeling, and performance evaluation. This review showed that support-vector-machine and deep-learning techniques have been well studied, to date.After reviewing over 200 papers, we also discussed the current challenges and opportunities in this field.

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Altered EEG power spectrum, but not sleep-wake architecture, in HCN1 knockout mice

Cited by 2 publications

References 32 publications

Mono and biallelic variants inHCN2cause severe neurodevelopmental disorders

Mono and biallelic variants inHCN2cause severe neurodevelopmental disorders

A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals

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