11 research outputs found
Sistema de predicción epileptogenica en lazo cerrado basado en matrices sub-durales
The human brain is the most complex organ in the human body, which consists of
approximately 100 billion neurons. These cells effortlessly communicate over multiple
hemispheres to deliver our everyday sensorimotor and cognitive abilities.
Although the underlying principles of neuronal communication are not well understood,
there is evidence to suggest precise synchronisation and/or de-synchronisation
of neuronal clusters could play an important role. Furthermore, new evidence suggests
that these patterns of synchronisation could be used as an identifier for the detection
of a variety of neurological disorders including, Alzheimers (AD), Schizophrenia (SZ)
and Epilepsy (EP), where neural degradation or hyper synchronous networks have
been detected.
Over the years many different techniques have been proposed for the detection of
synchronisation patterns, in the form of spectral analysis, transform approaches and
statistical based studies. Nonetheless, most are confined to software based implementations
as opposed to hardware realisations due to their complexity. Furthermore, the
few hardware implementations which do exist, suffer from a lack of scalability, in terms
of brain area coverage, throughput and power consumption.
Here we introduce the design and implementation of a hardware efficient algorithm,
named Delay Difference Analysis (DDA), for the identification of patient specific
synchronisation patterns. The design is remarkably hardware friendly when compared
with other algorithms. In fact, we can reduce hardware requirements by as much as
80% and power consumption as much as 90%, when compared with the most common
techniques. In terms of absolute sensitivity the DDA produces an average sensitivity
of more than 80% for a false positive rate of 0.75 FP/h and indeed up to a maximum
of 90% for confidence levels of 95%. This thesis presents two integer-based digital processors for the calculation of
phase synchronisation between neural signals. It is based on the measurement of time
periods between two consecutive minima. The simplicity of the approach allows for
the use of elementary digital blocks, such as registers, counters or adders. In fact,
the first introduced processor was fabricated in a 0.18μm CMOS process and only
occupies 0.05mm2 and consumes 15nW from a 0.5V supply voltage at a signal input
rate of 1024S/s. These low-area and low-power features make the proposed circuit a
valuable computing element in closed-loop neural prosthesis for the treatment of neural
disorders, such as epilepsy, or for measuring functional connectivity maps between
different recording sites in the brain.
A second VLSI implementation was designed and integrated as a mass integrated
16-channel design. Incorporated into the design were 16 individual synchronisation
processors (15 on-line processors and 1 test processor) each with a dedicated training
and calculation module, used to build a specialised epileptic detection system based
on patient specific synchrony thresholds. Each of the main processors are capable of
calculating the phase synchrony between 9 independent electroencephalography (EEG)
signals over 8 epochs of time totalling 120 EEG combinations. Remarkably, the entire
circuit occupies a total area of only 3.64 mm2.
This design was implemented with a multi-purpose focus in mind. Firstly, as a
clinical aid to help physicians detect pathological brain states, where the small area
would allow the patient to wear the device for home trials. Moreover, the small power
consumption would allow to run from standard batteries for long periods. The trials
could produce important patient specific information which could be processed using
mathematical tools such as graph theory. Secondly, the design was focused towards the
use as an in-vivo device to detect phase synchrony in real time for patients who suffer
with such neurological disorders as EP, which need constant monitoring and feedback.
In future developments this synchronisation device would make an good contribution
to a full system on chip device for detection and stimulation.El cerebro humano es el órgano más complejo del cuerpo humano, que consta
de aproximadamente 100 mil millones de neuronas. Estas células se comunican sin
esfuerzo a través de ambos hemisferios para favorecer nuestras habilidades sensoriales
y cognitivas diarias.
Si bien los principios subyacentes de la comunicación neuronal no se comprenden
bien, existen pruebas que sugieren que la sincronización precisa y/o la desincronización
de los grupos neuronales podrían desempeñar un papel importante. Además, nuevas
evidencias sugieren que estos patrones de sincronización podrían usarse como un identificador
para la detección de una gran variedad de trastornos neurológicos incluyendo
la enfermedad de Alzheimer(AD), la esquizofrenia(SZ) y la epilepsia(EP), donde se ha
detectado la degradación neural o las redes hiper sincrónicas.
A lo largo de los años, se han propuesto muchas técnicas diferentes para la detección
de patrones de sincronización en forma de análisis espectral, enfoques de transformación
y análisis estadísticos. No obstante, la mayoría se limita a implementaciones basadas
en software en lugar de realizaciones de hardware debido a su complejidad. Además,
las pocas implementaciones de hardware que existen, sufren una falta de escalabilidad,
en términos de cobertura del área del cerebro, rendimiento y consumo de energía.
Aquí presentamos el diseño y la implementación de un algoritmo eficiente de
hardware llamado “Delay Difference Aproximation” (DDA) para la identificación
de patrones de sincronización específicos del paciente. El diseño es notablemente
compatible con el hardware en comparación con otros algoritmos. De hecho, podemos
reducir los requisitos de hardware hasta en un 80% y el consumo de energía hasta en
un 90%, en comparación con las técnicas más comunes. En términos de sensibilidad
absoluta, la DDA produce una sensibilidad promedio de más del 80% para una tasa de
falsos positivos de 0,75 PF / hr y hasta un máximo del 90% para niveles de confianza
del 95%.
Esta tesis presenta dos procesadores digitales para el cálculo de la sincronización de
fase entre señales neuronales. Se basa en la medición de los períodos de tiempo entre dos
mínimos consecutivos. La simplicidad del enfoque permite el uso de bloques digitales
elementales, como registros, contadores o sumadores. De hecho, el primer procesador
introducido se fabricó en un proceso CMOS de 0.18μm y solo ocupa 0.05mm2 y consume
15nW de un voltaje de suministro de 0.5V a una tasa de entrada de señal de 1024S/s Estas características de baja área y baja potencia hacen que el procesador propuesto
sea un valioso elemento informático en prótesis neurales de circuito cerrado para el
tratamiento de trastornos neuronales, como la epilepsia, o para medir mapas de
conectividad funcional entre diferentes sitios de registro en el cerebro.
Además, se diseñó una segunda implementación VLSI que se integró como un
diseño de 16 canales integrado en masa. Se incorporaron al diseño 16 procesadores
de sincronización individuales (15 procesadores en línea y 1 procesador de prueba),
cada uno con un módulo de entrenamiento y cálculo dedicado, utilizado para construir
un sistema de detección epiléptico especializado basado en umbrales de sincronía
específicos del paciente. Cada uno de los procesadores principales es capaz de calcular
la sincronización de fase entre 9 señales de electroencefalografía (EEG) independientes
en 8 épocas de tiempo que totalizan 120 combinaciones de EEG. Cabe destacar que
todo el circuito ocupa un área total de solo 3.64 mm2.
Este diseño fue implementado teniendo en mente varios propósitos. En primer
lugar, como ayuda clínica para ayudar a los médicos a detectar estados cerebrales
patológicos, donde el área pequeña permitiría al paciente usar el dispositivo para las
pruebas caseras. Además, el pequeño consumo de energía permitiría una carga cero del
dispositivo, lo que le permitiría funcionar con baterías estándar durante largos períodos.
Los ensayos podrían producir información importante específica para el paciente que
podría procesarse utilizando herramientas matemáticas como la teoría de grafos. En
segundo lugar, el diseño se centró en el uso como un dispositivo in-vivo para detectar la
sincronización de fase en tiempo real para pacientes que sufren trastornos neurológicos
como el EP, que necesitan supervisión y retroalimentación constantes. En desarrollos
futuros, este dispositivo de sincronización sería una buena base para desarrollar un
sistema completo de un dispositivo chip para detección de trastornos neurológicos
Phase Synchronization Operator for On-Chip Brain Functional Connectivity Computation
This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters, and adders. The processor, fabricated in a 0.18- μ m CMOS process, only occupies 0.05 mm 2 and consumes 15 nW from a 0.5 V supply voltage at a signal input rate of 1024 S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research (USA) N00014-19-1-215
Real-time phase correlation based integrated system for seizure detection
This paper reports a low area, low power, integer-based digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. In fact, the processor, fabricated in a 0.18μm CMOS process, only occupies an area of 0.0625μm2 and consumes 12.5nW from a 1.2V supply voltage when operated at 128kHz. These low-area, low-power features make the proposed processor a valuable computing element in closed loop neural prosthesis for the treatment of neural diseases, such as epilepsy, or for extracting functional connectivity maps between different recording sites in the brain.Ministerio de Economía y Competitividad TEC2016- 80923-
Integer-based digital processor for the estimation of phase synchronization between neural signals
This paper reports a low area, low power, integer-based neural digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. The low area and power consumptions make the processor an extremely scalable device which would work well in closed loop neural prosthesis for the treatment of neural diseases.Ministerio de Ciencia e Innovación TEC2012-33634Office of Naval Research (USA) N00014111031
Highly scalable real time epilepsy diagnosis architecture via phase correlation
Epilepsy is at current the world’s second most common neurological disorder affecting an estimated 50 million people. While up to 70% of epileptic suffers are treated successfully with epileptic medication some 30% continue to suffer untreated [1]. This gap could be filled by the implementation of implantable neural prostheses which are able to detect when a seizure is coming and eventually actuate in the brain to stop its progression. The change in brain activity during epileptic fits has been leading scientists to investigate neural features such as neural spiking [2], correlation [3] and the most tantalizing, phase synchronization, in order to predict seizures before they happen. As described in [4], a large decrease in synchronization between two neural signals can be seen for an unknown period during the pre-ictal stage. This decrease in synchronization is believed to be a significant bio-marker which could hold the key to prediction and prevention of epileptic seizures via neural prosthesis. The discrete distance approximation (DDA) algorithm proposed in this work can drastically reduce the number of complex operations (multiplications and divisions), relying only on basic addition, comparison and shifting. In terms of logic, the DDA can reduce the amount of hardware needed to detect pre-ictal events by as much as 96.8% when compared to systems with similar functionality. Due to its highly efficient area and power consumption, the proposed approach could lead to a truly functional medical in-vivo application for real time monitoring and or prevention.This work has been funded by Mineco under grant TEC2012-33634, Junta de Andalucía under project TIC 2338, the Office of Naval Research (ONR -USA) under Project N00014-14-1-0355 and the FEDER Program.Peer reviewe
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The extreme-ultraviolet stellar characterization for atmospheric physics and evolution (ESCAPE) mission concept
The long-term stability of exoplanetary atmospheres depends critically on the extreme-ultraviolet (EUV) flux from the host star. The EUV flux likely controls the demographics of the short-period planet population as well the ability for rocky planets to maintain habitable environments long enough for the emergence of life. We present the Extreme-ultraviolet Stellar Characterization for Atmospheric Physics and Evolution (ESCAPE) mission, an astrophysics Small Explorer proposed to NASA. ESCAPE employs extreme-and far-ultraviolet spectroscopy (70 - 1800 angstrom) to characterize the high-energy radiation environment in the habitable zones (HZs) around nearby stars. ESCAPE provides the first comprehensive study of the stellar EUV environments that control atmospheric mass-loss and determine the habitability of rocky exoplanets. The ESCAPE instrument comprises an EUV grazing incidence telescope feeding four diffraction gratings and a photon-counting detector. The telescope is 50 cm diameter with four nested parabolic primary mirrors and four nested elliptical secondary mirrors, fabricated and aligned by NASA Marshall Space Flight Center and the Smithsonian Astrophysical Observatory. The off-plane grating assemblies are fabricated at Pennsylvania State University and the ESCAPE detector system is a micro-channel plate (MCP; 125mm x 40mm active area) sensor developed by the University of California, Berkeley. ESCAPE employs the versatile and high-heritage Ball Aerospace BCP-100 spacecraft.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Signatures of miR-181a on renal transcriptome and blood pressure.
MicroRNA-181a binds to the 3’ untranslated region of messenger RNA (mRNA) for renin, a rate-limiting enzyme of the renin-angiotensin system. Our objective was to determine whether this molecular interaction translates into a clinically meaningful effect on blood pressure and whether circulating miR-181a is a measurable proxy of blood pressure. In 200 human kidneys from the TRANScriptome of renaL humAn TissuE (TRANSLATE) study, renal miR-181a was the sole negative predictor of renin mRNA and a strong correlate of circulating miR-181a. Elevated miR-181a levels correlated positively with systolic and diastolic blood pressure in TRANSLATE, and this association was independent of circulating renin. The association between serum miR-181a and systolic blood pressure was replicated in 199 subjects from the Genetic Regulation of Arterial Pressure of Humans In the Community (GRAPHIC) study. Renal immunohistochemistry and in situ hybridization showed that colocalization of miR-181a and renin was most prominent in collecting ducts where renin is not released into the systemic circulation. Analysis of 69 human kidneys characterized by RNA sequencing revealed that miR-181a was associated with downregulation of four mitochondrial pathways and upregulation of 41 signaling cascades of adaptive immunity and inflammation. We conclude that renal miR-181a has pleiotropic effects on pathways relevant to blood pressure regulation and that circulating levels of miR-181a are both a measurable proxy of renal miR-181a expression and a novel biochemical correlate of blood pressure