A Novel Modular Headmount Design for non-invasive Scalp EEG Recordings in Awake Animal Models*

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

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Differences in Cardiac Effects of Venoms from Tentacles and the Bell of Live Carukia barnesi: Using Non-Invasive Pulse Wave Doppler

Toxins ◽

10.3390/toxins13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Mark Little ◽

Peter Pereira ◽

Jamie Seymour

Keyword(s):

Animal Models ◽

Murine Model ◽

Real World ◽

Cardiac Dysfunction ◽

Pulse Wave ◽

Cardiac Effects ◽

Post Exposure ◽

Non Invasive ◽

Cardiac Decompensation

Carukia barnesi was the first in an expanding list of cubozoan jellyfish whose sting was identified as causing Irukandji syndrome. Nematocysts present on both the bell and tentacles are known to produce localised stings, though their individual roles in Irukandji syndrome have remained speculative. This research examines differences through venom profiling and pulse wave Doppler in a murine model. The latter demonstrates marked measurable differences in cardiac parameters. The venom from tentacles (CBVt) resulted in cardiac decompensation and death in all mice at a mean of 40 min (95% CL: ± 11 min), whereas the venom from the bell (CBVb) did not produce any cardiac dysfunction nor death in mice at 60 min post-exposure. This difference is pronounced, and we propose that bell exposure is unlikely to be causative in severe Irukandji syndrome. To date, all previously published cubozoan venom research utilised parenterally administered venom in their animal models, with many acknowledging their questionable applicability to real-world envenomation. Our model used live cubozoans on anaesthetised mice to simulate normal envenomation mechanics and actual expressed venoms. Consequently, we provide validity to the parenteral methodology used by previous cubozoan venom research.

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Neural silences can be localized rapidly using noninvasive scalp EEG

Communications Biology ◽

10.1038/s42003-021-01768-0 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Alireza Chamanzar ◽

Marlene Behrmann ◽

Pulkit Grover

Keyword(s):

Center Of Mass ◽

Cost Effective ◽

Human Head ◽

Head Model ◽

Cortical Function ◽

Scalp Eeg ◽

Localization Algorithms ◽

Important Benefit ◽

Eeg Recordings ◽

Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

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Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

Scientific Reports ◽

10.1038/s41598-021-83337-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jan Pyrzowski ◽

Jean- Eudes Le Douget ◽

Amal Fouad ◽

Mariusz Siemiński ◽

Joanna Jędrzejczak ◽

...

Keyword(s):

Normal Subjects ◽

Intracranial Eeg ◽

Zero Crossing ◽

Temporal Precision ◽

Scalp Eeg ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Recordings ◽

Patients With Epilepsy ◽

Low Amplitude

AbstractClinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

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EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

International Journal of Neural Systems ◽

10.1142/s0129065714500233 ◽

2014 ◽

Vol 24 (07) ◽

pp. 1450023 ◽

Cited By ~ 14

Author(s):

LUNG-CHANG LIN ◽

CHEN-SEN OUYANG ◽

CHING-TAI CHIANG ◽

REI-CHENG YANG ◽

RONG-CHING WU ◽

...

Keyword(s):

Refractory Epilepsy ◽

Recall Rate ◽

Idiopathic Epilepsy ◽

Spectral Edge Frequency ◽

Scalp Eeg ◽

Feature Descriptors ◽

Antiepileptic Drug Treatment ◽

Eeg Recordings ◽

Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

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Non-invasive evaluation of placental blood flow: lessons from animal models

Reproduction ◽

10.1530/rep-16-0428 ◽

2017 ◽

Vol 153 (3) ◽

pp. R85-R96 ◽

Cited By ~ 8

Author(s):

E Mourier ◽

A Tarrade ◽

J Duan ◽

C Richard ◽

C Bertholdt ◽

...

Keyword(s):

Blood Flow ◽

Animal Models ◽

Ultrasound Contrast Agents ◽

Valuable Insight ◽

Non Invasive ◽

Placental Blood ◽

2D And 3D ◽

Reliable Methods ◽

Placental Blood Flow ◽

Invasive Evaluation

In human obstetrics, placental vascularisation impairment is frequent as well as linked to severe pathological events (preeclampsia and intrauterine growth restriction), and there is a need for reliable methods allowing non-invasive evaluation of placental blood flow. Uteroplacental vascularisation is complex, and animal models are essential for the technical development and safety assessment of these imaging tools for human clinical use; however, these techniques can also be applied in the veterinary context. This paper reviews how ultrasound-based imaging methods such as 2D and 3D Doppler can provide valuable insight for the exploration of placental blood flow both in humans and animals and how new approaches such as the use of ultrasound contrast agents or ultrafast Doppler may allow to discriminate between maternal (non-pulsatile) and foetal (pulsatile) blood flow in the placenta. Finally, functional magnetic resonance imaging could also be used to evaluate placental blood flow, as indicated by studies in animal models, but its safety in human pregnancy still requires to be confirmed.

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Manifestation of Hippocampal Interictal Discharges on Clinical Scalp EEG Recordings

Journal of Clinical Neurophysiology ◽

10.1097/wnp.0000000000000867 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Somin Lee ◽

Shasha Wu ◽

James X. Tao ◽

Sandra Rose ◽

Peter C. Warnke ◽

...

Keyword(s):

Scalp Eeg ◽

Eeg Recordings ◽

Interictal Discharges

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Positron Emission Tomography in Animal Models of Alzheimer’s Disease Amyloidosis

10.20944/preprints202110.0222.v1 ◽

2021 ◽

Author(s):

Ruiqing Ni

Keyword(s):

Positron Emission Tomography ◽

Animal Models ◽

Amyloid Beta ◽

Emission Tomography ◽

Imaging Biomarkers ◽

Preclinical Research ◽

Non Invasive ◽

Positron Emission ◽

Cerebral Amyloid ◽

On Chip

Animal models of Alzheimer’s disease amyloidosis that recapitulate cerebral amyloid-beta pathology have been widely used in preclinical research, and have greatly enabled the mechanistic understanding of Alzheimer’s disease and the development of therapeutics. Comprehensive deep phenotyping of the pathophysiological and biochemical features in these animal models are essential. Recent advances in positron emission tomography have allowed the non-invasive visualization of the alterations in the brain of animal models as well as in patients with Alzheimer’s disease, These tools have facilitated our understanding of disease mechanisms, and provided longitudinal monitoring of treatment effect in animal models of Alzheimer’s disease amyloidosis. In this review, we focus on recent positron emission tomography studies of cerebral amyloid-beta accumulation, hypoglucose metabolism, synaptic and neurotransmitter receptor deficits (cholinergic and glutamatergic system), blood-brain barrier impairment and neuroinflammation (microgliosis and astrocytosis) in animal models of Alzheimer’s disease amyloidosis. We further propose the emerging targets and tracers for reflecting the pathophysiological changes, and discuss outstanding challenges in disease animal models and future outlook in on-chip characterization of imaging biomarkers towards clinical translation.

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ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

10.1101/2020.12.21.423728 ◽

2020 ◽

Author(s):

Poomipat Boonyakitanont ◽

Apiwat Lek-uthai ◽

Jitkomut Songsiri

Keyword(s):

Neural Network ◽

Epileptic Seizure ◽

False Positive Rate ◽

Imbalanced Data ◽

Detection Algorithm ◽

Processing Technique ◽

Post Processing ◽

Scalp Eeg ◽

Positive Rate ◽

Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

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