Building on Progress Towards Fair Access : annual report 2019

Supplementary Table 3: Anti-epileptic drug use in patients with SYNGAP1 mutations or deletion

Data_Sheet_1_Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.PDF

IntroductionEpilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure detection algorithm is of great significance. Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to better handle brain signals and benefit real-world, low-power applications. However, automatic epilepsy seizure detection rarely considers SNNs.MethodsIn this article, we have explored SNNs for cross-patient seizure detection and discovered that SNNs can achieve comparable state-of-the-art performance or a performance that is even better than artificial neural networks (ANNs). We propose an EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals.ResultsWe extensively evaluate the performance of different SNN structures, training methods, and time settings, which builds a solid basis for understanding and evaluation of SNNs in seizure detection. Moreover, we show that our EESNN model can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation.DiscussionThese results show the potential for building high-performance, low-power neuromorphic systems for seizure detection and also broaden real-world application scenarios of SNNs.</p

Electropherograms showing mutations in the <i>ALDH7A1</i> gene in 3 patients.

<p>A: c.410G>A (p.G137E), IVS11+1G>A (inverting sequencing) in case 1; B: heterozygous c.952 G>C (p.A318P), heterozygous c.965 C>T (p.A322V) in case 2; C: heterozygous c.902A>T (p.N301I), IVS11+1G>A (inverting sequencing) in case 3.</p

The effect of low intracellular Mg²⁺ on NMDAR model.

<p>PreV: the voltage of the presynaptic neuron, induced to fire in three bursts of action potentials; PostV: the postsynaptic potential from NMDA synaptic current in response to input from presynaptic cell above. Black trace was the response in normal intracellular Mg²⁺ (1 mM) and the red trace was the response in low intracellular Mg²⁺ (0.1 mM). The amplitude of postsynaptic potential increased as the intracellular Mg²⁺ concentration dropped to one tenth the normal value by unblocking the NMDAR current.</p

The concentration of extra/intracellular Mg²⁺ measured by ICP-OES.

<p>There was no significant change of extracellular Mg²⁺ concentration among the groups compared to naïve cells; the mutant N244S and Ins dramatically decreased the concentration of intracellular Mg²⁺, though the mutant I178F and the overexpression group were not different from the naïve group. Naïve: the cultured neurons without transfection; Overexpression: neurons transfected with <i>NIPA2</i> (WT); Ins: N334_E335insD. siRNA: neurons transfected with <i>NIPA2</i>-siRNA; the group (WT+siRNA): neurons transfected with <i>NIPA2</i>^WT and <i>NIPA2</i>-siRNA; the mutant+siRNA group (I178F+siRANA, N244S+siRNA, Ins+siRNA): neurons transfected with the mutant and <i>NIPA2</i>-siRNA. Relative concentration was calculated as: Mg²⁺ concentration of the transfected group/Mg²⁺ concentration of naïve group. n = 4 experiments.</p

Statistics for protein expressions.

<p>We defined WT as 1. Except for R22Q, all other mutations resulted in significant downregulation in mutant MLC1 protein levels (n = 4). The statistics of these data was compiled by T test in Prism 5 (*** <i>P<0.001</i>, ** <i>P<0.01</i>, * <i>P<0.05</i>, compared with WT).</p