Signals from Intraventricular Depth Electrodes Can Control a Brain-Computer Interface

A Brain-Computer Interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans have used scalp-recorded electroencephalography (EEG). We have recently demonstrated that signals from intracranial electrocorticography (ECoG) and stereotactic depth electrodes (SDE) in the hippocampus can be used to control a BCI P300 Speller paradigm. We report a case in which stereotactic depth electrodes positioned in the ventricle were able to obtain viable signals for a BCI. Our results demonstrate that event-related potentials from intraventricular electrodes can be used to reliably control the P300 Speller BCI paradigm

Antiepileptic drugs and suicidality

The risk of suicide in patients with epilepsy is significantly higher than the general population. There are many hypotheses as to the reasons for this, but the potential role of anti-epileptic drugs (AEDs) in increasing suicidality has recently been brought into question. In 2008, the U.S. Food and Drug Administration (FDA) published a warning after a meta-analysis of data from all clinical trials involving AEDs found a suicidality risk of 0.43 per 1000 patients in active drug arms of these clinical trials compared to a rate in the placebo arm of 0.22. While an increased risk for individual AEDs was found in two, the FDA decided to issue a warning for the entire AED class. While this decision and the meta-analysis findings have been considered controversial, and have created concern that this stated risk may dissuade use of AEDs by patients who would benefit from them, it has led to increased awareness of the risk of suicidality and psychiatric co-morbidity in this patient group. In this article, the association of epilepsy and AEDs with psychiatric disease and suicidality are reviewed, perspective as to the significance and limitations of the FDA’s findings are discussed, and some options for suicidality screening and their potential utility in clinical care are evaluated

Empirical Models of Scalp-EEG Responses Using Non-Concurrent Intracranial Responses

Objective- This study presents inter-subject models of scalp-recorded electroencephalographic (sEEG) event-related potentials (ERPs) using intracranially recorded ERPs from electrocorticography and stereotactic depth electrodes in the hippocampus, generally termed as intracranial EEG (iEEG). Approach- The participants were six patients with medically-intractable epilepsy that underwent temporary placement of intracranial electrode arrays to localize seizure foci. Participants performed one experimental session using a brain-computer interface matrix spelling paradigm controlled by sEEG prior to the iEEG electrode implantation, and one or more identical sessions controlled by iEEG after implantation. All participants were able to achieve excellent spelling accuracy using sEEG, four of the participants achieved roughly equivalent performance in the iEEG sessions, and all participants were significantly above chance accuracy for the iEEG sessions. The sERPs were modeled using a linear combination of iERPs using two different optimization criteria. Main results- The results indicate that sERPs can be accurately estimated from the iERPs for the patients that exhibited stable ERPs over the respective sessions, and that the transformed iERPs can be accurately classified with an sERP-derived classifier. Significance- The resulting models provide a new empirical representation of the formation and distribution of sERPs from underlying composite iERPs. These new insights provide a better understanding of ERP relationships and can potentially lead to the development of more robust signal processing methods for noninvasive EEG applications

Brain-Computer Interfaces in Medicine

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroenceph-alography-based spelling and single-neuron-based device control, researchers have gone on to use electroenceph-alographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function

Direct Classification of All American English Phonemes Using Signals From Functional Speech Motor Cortex

Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we investigated words that span the entire set of phonemes in the General American accent using ECoG with 4 subjects. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits/s (33.6 words min), supporting pursuit of speech articulation for BCI control

final safety tolerability and seizure outcomes in patients with focal epilepsy treated with adjunctive perampanel for up to 4 years in an open label extension of phase iii randomized trials study 307

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Preparation of Bismuth Oxide Photocatalyst and Its Application in White-light LEDs

Bismuth oxide photocatalysts were synthesized and coated on the front surface of phosphor-converted white light-emitting diodes to produce a safe and environmentally benign lighting source. Bismuth oxide photocatalyst powders were synthesized with a spray pyrolysis method at 500°C, 600°C, 700°C, and 800°C. Using the absorption spectrum in the blue and UV regions of the bismuth oxide photocatalysts, the blue light and UV leakage problems of phosphor-converted white LEDs can be significantly reduced. The experimental results showed that bismuth oxide photocatalyst synthesized at 700°C exhibited the most superior spectrum inhibiting ability. The suppressed ratio reached 52.33% in the blue and UV regions from 360 to 420 nm. Related colorimetric parameters and the photocatalyst decomposition ability of fabricated white-light LEDs were tested. The CIE chromaticity coordinates (x,y) were (0.349, 0.393), and the correlated color temperature was 4991 K. In addition, the coating layer of photocatalyst can act as an air purifier and diffuser to reduce glare. A value of 66.2±0.60 ppmv of molecular formaldehyde gas can be decomposed in 120 mins

Corpora amylacea are associated with tau burden and cognitive status in Alzheimer\u27s disease

Corpora amylacea (CA) and their murine analogs, periodic acid Schiff (PAS) granules, are age-related, carbohydrate-rich structures that serve as waste repositories for aggregated proteins, damaged cellular organelles, and other cellular debris. The structure, morphology, and suspected functions of CA in the brain imply disease relevance. Despite this, the link between CA and age-related neurodegenerative diseases, particularly Alzheimer\u27s disease (AD), remains poorly defined. We performed a neuropathological analysis of mouse PAS granules and human CA and correlated these findings with AD progression. Increased PAS granule density was observed in symptomatic tau transgenic mice and APOE knock-in mice. Using a cohort of postmortem AD brain samples, we examined CA in cognitively normal and dementia patients across Braak stages with varying APOE status. We identified a Braak-stage dependent bimodal distribution of CA in the dentate gyrus, with CA accumulating and peaking by Braak stages II-III, then steadily declining with increasing tau burden. Refined analysis revealed an association of CA levels with both cognition and APOE status. Finally, tau was detected in whole CA present in human patient cerebrospinal fluid, highlighting CA-tau as a plausible prodromal AD biomarker. Our study connects hallmarks of the aging brain with the emergence of AD pathology and suggests that CA may act as a compensatory factor that becomes depleted with advancing tau burden