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Register a new userNoninvasive optical estimation of CSF thickness for brain-atrophy monitoring
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Dementia disorders are increasingly becoming sources of a broad range of problems, strongly interfering with normal daily tasks of a growing number of individuals. Such neurodegenerative diseases are often accompanied with progressive brain atrophy that, at late stages, leads to drastically reduced brain dimensions. At the moment, this structural involution can be followed with XCT or MRI measurements that share numerous disadvantages in terms of usability, invasiveness and costs. In this work, we aim to retrieve information concerning the brain atrophy stage and its evolution, proposing a novel approach based on non-invasive time-resolved Near Infra-Red (tr-NIR) measurements. For this purpose, we created a set of human-head atlases, in which we eroded the brain as it would happen in a clinical brain-atrophy progression. With these realistic meshes, we reproduced a longitudinal tr-NIR study exploiting a Monte-Carlo photon propagation algorithm to model the varying cerebral spinal fluid (CSF). The study of the time-resolved reflectance curve at late photon arrival times exhibited peculiar slope-changes upon CSF layer increase that were confirmed under several measurement conditions. The performance of the technique suggests good sensitivity to CSF variation, useful for a fast and non-invasive observation of the dementia progression.
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Traumatic brain injury [TBI] has become a signature injury of current military conflicts, with debilitating, costly, and long-lasting effects. Although mechanisms by which head impacts cause TBI have been well-researched, the mechanisms by which blasts cause TBI are not understood. From numerical hydrodynamic simulations, we have discovered that non-lethal blasts can induce sufficient skull flexure to generate potentially damaging loads in the brain, even without a head impact. The possibility that this mechanism may contribute to TBI has implications for injury diagnosis and armor design.
We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as input, the network learns to deform images according to a Neo-Hookean model of tissue deformation. The tool is validated using longitudinal brain atrophy data from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, and we demonstrate that the trained model is capable of rapidly simulating new brain deformations with minimal residuals. This method has the potential to be used in data augmentation or for the exploration of different causal hypotheses reflecting brain growth and atrophy.
We propose a model of brain atrophy as a function of high-dimensional genetic information and low dimensional covariates such as gender, age, APOE gene, and disease status. A nonparametric single-index Bayesian model of high dimension is proposed to model the relationship with B-spline series prior on the unknown functions and Dirichlet process scale mixture of centered normal prior on the distributions of the random effects. The posterior rate of contraction without the random effect is established for a fixed number of regions and time points with increasing sample size. We implement an efficient computation algorithm through a Hamiltonian Monte Carlo (HMC) algorithm. The performance of the proposed Bayesian method is compared with the corresponding least square estimator in the linear model with horseshoe prior, LASSO and SCAD penalization on the high-dimensional covariates. The proposed Bayesian method is applied to a dataset on volumes of brain regions recorded over multiple visits of 748 individuals using 620,901 SNPs and 6 other covariates for each individual, to identify factors associated with brain atrophy.
The new era of artificial intelligence demands large-scale ultrafast hardware for machine learning. Optical artificial neural networks process classical and quantum information at the speed of light, and are compatible with silicon technology, but lack scalability and need expensive manufacturing of many computational layers. New paradigms, as reservoir computing and the extreme learning machine, suggest that disordered and biological materials may realize artificial neural networks with thousands of computational nodes trained only at the input and at the readout. Here we employ biological complex systems, i.e., living three-dimensional tumour brain models, and demonstrate a random neural network (RNN) trained to detect tumour morphodynamics via image transmission. The RNN, with the tumour spheroid as a three-dimensional deep computational reservoir, performs programmed optical functions and detects cancer morphodynamics from laser-induced hyperthermia inaccessible by optical imaging. Moreover, the RNN quantifies the effect of chemotherapy inhibiting tumour growth. We realize a non-invasive smart probe for cytotoxicity assay, which is at least one order of magnitude more sensitive with respect to conventional imaging. Our random and hybrid photonic/living system is a novel artificial machine for computing and for the real-time investigation of tumour dynamics.
This paper presents an analytical design of an ultrasonic power transfer system based on piezoelectric micro-machined ultrasonic transducer (PMUT) for fully wireless brain implants in mice. The key steps like the material selection of each layer and the top electrode radius to maximize the coupling factor are well-detailed. This approach results in the design of a single cell with a high effective coupling coefficient. Furthermore, compact models are used to make the design process less time-consuming for designers. These models are based on the equivalent circuit theory for the PMUT. A cell of 107 um in radius, 5 um in thickness of Lead Zirconate Titanium (PZT), and 10 um in thickness of silicon (Si) is found to have a 4% of effective coupling coefficient among the highest values for a clamped edge boundary conditions. Simulation results show a frequency of 2.84 MHz as resonance. In case of an array, mutual impedance and numerical modeling are used to estimate the distance between the adjacent cells. In addition, the area of the proposed transducer and the number of cells are computed with the Rayleigh distance and neglecting the cross-talk among cells, respectively. The designed transducer consists of 7x7 cells in an area of 3.24 mm2. The transducer is able to deliver an acoustic intensity of 7.185 mW/mm2 for a voltage of 19.5 V for powering brain implants seated in the motor cortex and striatum of the mices brain. The maximum acoustic intensity occurs at a distance of 2.5 mm in the near field which was estimated with the Rayleigh length equation.
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