Extent of single-neuron activity modulation by hippocampal interictal discharges predicts declarative memory disruption in humans

Memory deficits are common in epilepsy patients. In these patients, the interictal EEG commonly shows interictal epileptiform discharges (IEDs). While IEDs are associated with transient cognitive impairments, it remains poorly understood why this is. We investigated the effects of human (male and female) hippocampal IEDs on single-neuron activity during a memory task in patients with medically refractory epilepsy undergoing depth electrode monitoring. We quantified the effects of hippocampal IEDs on single-neuron activity and the impact of this modulation on subjectively declared memory strength. Across all recorded neurons, the activity of 50 of 728 neurons were significantly modulated by IEDs, with the strongest modulation in the medial temporal lobe (33 of 416) and in particular the right hippocampus (12 of 58). Putative inhibitory neurons, as identified by their extracellular signature, were more likely to be modulated by IEDs than putative excitatory neurons (19 of 157 vs 31 of 571). Behaviorally, the occurrence of hippocampal IEDs was accompanied by a disruption of recognition of familiar images only if they occurred up to 2 s before stimulus onset. In contrast, IEDs did not impair encoding or recognition of novel images, indicating high temporal and task specificity of the effects of IEDs. The degree of modulation of individual neurons by an IED correlated with the declared confidence of a retrieval trial, with higher firing rates indicative of reduced confidence. Together, these data link the transient modulation of individual neurons by IEDs to specific declarative memory deficits in specific cell types, thereby revealing a mechanism by which IEDs disrupt medial temporal lobe-dependent declarative memory retrieval processes

Los archivalia de Arias Montano en el Museo Plantin-Moretus de Amberes

En el presente trabajo ofrecemos un recorrido por los documentos de Benito Arias Montano conservados en el Museo Plantin-Moretus de Amberes. De los riquísimos fondos de esta institución existen ya dos inventarios, uno de manuscritos y otro de archivos. Sin embargo, la descripción bibliográfica que estos ofrecen dista mucho de ser exhaustiva; más bien se queda en la superficie. En una comparación fácil con la arqueología, se puede decir que los primeros conservadores de dicho museo realizaron una primera prospección horizontal y a cada uno de los investigadores les toca excavar en dirección vertical la parcela que les interese. Los resultados de nuestra búsqueda, centrada en Arias Montano, pueden agruparse en cinco bloques: correspondencia, pedidos de libros, documentos comerciales, cuentas con la casa plantiniana y un apartado misceláneo que llamamos varia

Improving P300 spelling rate using language models and predictive spelling

The P300 Speller Brain-Computer Interface (BCI) provides a means of communication for those suffering from advanced neuromuscular diseases such as amyotrophic lateral sclerosis (ALS). Recent literature has incorporated language-based modelling, which uses previously chosen characters and the structure of natural language to modify the interface and classifier. Two complementary methods of incorporating language models have previously been independently studied: predictive spelling uses language models to generate suggestions of complete words to allow for the selection of multiple characters simultaneously, and language model-based classifiers have used prior characters to create a prior probability distribution over the characters based on how likely they are to follow. In this study, we propose a combined method which extends a language-based classifier to generate prior probabilities for both individual characters and complete words. In order to gauge the efficiency of this new model, results across 12 healthy subjects were measured. Incorporating predictive spelling increased typing speed using the P300 speller, with an average increase of 15.5% in typing rate across subjects, demonstrating that language models can be effectively utilized to create full word suggestions for predictive spelling. When combining predictive spelling with language model classification, typing speed is significantly improved, resulting in better typing performance

Box plots of the optimal selection rates, accuracies, and correct characters per minute (CCPM) for offline trials using the inverted (Inv) and famous faces (FF) flashing paradigms with either the SWLDA or particle filtering (PF) classifiers with dynamic stopping.

<p>Box plots of the optimal selection rates, accuracies, and correct characters per minute (CCPM) for offline trials using the inverted (Inv) and famous faces (FF) flashing paradigms with either the SWLDA or particle filtering (PF) classifiers with dynamic stopping.</p

A comparison of stimulus types in online classification of the P300 speller using language models - Fig 1

<p><b>Screenshots of a stimulus presentation using Non-Inverting (a), Inverting (b), and Famous Faces (c).</b> In the experiment, an image of Einstein was used for the famous faces paradigm, which is replaced here with an image of one of the authors due to print license. The individual pictured has given written informed consent (as outlined in the PLOS consent form) to publish their image.</p

Online selection rates, accuracies, and correct characters per minute (CCPM) for each subject using the inverted and famous faces flashing paradigms with the particle filtering classifier.

<p>Online selection rates, accuracies, and correct characters per minute (CCPM) for each subject using the inverted and famous faces flashing paradigms with the particle filtering classifier.</p