Brain multiphysics最新文献

The use of ultrasound as a non-invasive means to modulate neuronal electrophysiological signals in experimental in vivo and in vitro models has recently been gaining momentum. Paradoxically, the intrinsic mechanisms linking high-frequency minute mechanical vibrations to electrophysiological alterations at the cellular scale are yet to be identified in this context. To this end, this work combines patch clamp and nanoindentation to study the action potential alterations induced by direct mechanical vibrations at ultrasonic frequencies of dorsal root ganglion-derived neuronal single cells. The characteristics of the action potentials are studied under oscillatory shear loadings of 25 and 50 nm displacement amplitudes at frequencies ranging from 250 kHz to 1 MHz. Results show significantly narrower action potentials, with faster depolarisations and shorter rising and falling phases when induced after 1 MHz. The faster action potential dynamics appearing once the oscillation is removed points towards a cumulative or lagged effect of mechanical stimulation at ultrasonic frequencies, also observed in ultrasound neuromodulation studies. It is hypothesised here that this action potential modulation arises as a consequence of remarkable membrane properties changes induced above a threshold frequency, situated between 370 kHz and 960 kHz, and possibly related to membrane stiffening and membrane phase state alterations. These observations demonstrate the ability of mechanical cues at the cellular level to modify the neuronal signal and assert the importance of the direct mechanical vibrations induced by ultrasound stimulation protocols in assisting the observed neuromodulatory effects.

Statement of Significance

In the last few decades, transcranial ultrasound stimulation (TUS) has established itself as one of the most promising non-invasive neuromodulating techniques. In particular, by avoiding both the lack of spatial specificity and surgical needs plaguing other established techniques, TUS offers new avenues for the treatment of neurological diseases. In order to enhance its specificity and efficacy, and, ultimately, optimise the sonication parameters for a given application, a better understanding of the underlying mechanisms linking mechanical vibrations to electrophysiological alterations is needed. By focusing on this coupling down to the cellular scale, this work demonstrates at the cellular scale that a transition between $\sim$ 400 kHz and $\sim$ 1 MHz exists above which mechanical vibrations are able to modulate the neuronal action potential by accelerating its dynamics.

Perceptual decision-making employs a range of higher order metacognitive processes. Two of the most important of these are perceptual awareness; or the clarity with which one reports seeing a perceptual stimulus, and response confidence; or the certainty one has about the correctness of one's own perceptual categorizations. We used a novel false feedback paradigm to investigate the relationships between these two processes. We asked people to perform a standard psychophysical detection task. We used feedback to selectively intervene either on our participants’ trust in their own perceptual awareness of the stimulus, or on their confidence in their own responses. We measured the effects of these interventions on response accuracy; on reports of perceptual awareness; and on response confidence. We found that by undermining people's trust in their awareness of the sensory stimulus, we could reliably reduce their accuracy on the task. We suggest that the reason this occurred is that people came to rely less on evidence from their senses when making perceptual decisions. We conclude by suggesting that there is a not a one-to-one mapping between content in conscious experience and how that content is used in perceptual decision making, and that one's perception of the reliability of content also plays a role.

Statement of Significance

This paper explores how different kinds of metacognitive state are related to one another and to perceptual decision making. Our focus is on the states of metacognitive confidence and perceptual awareness. We examine how an intervention on the reliability of these states influences performance in a perceptual detection task. We also examine how the intervention influences reports of the states themselves. The intervention we use is false feedback. For one group of participants, we tell them their perceptual judgement is wrong whenever they report they are uncertain in their choice (confidence intervention). For another group, we tell them their judgement is wrong whenever they report that their experience of the stimulus is unclear (awareness intervention). We find that both interventions reduce the accuracy of people's judgements, but that the awareness intervention is more effective. Also, we find that only the awareness intervention reduces reports of both metacognitive confidence in the response, and awareness of the stimuli. The confidence intervention does not influence either metacognitive state. These results suggest that we should understand confidence and awareness as separate higher level cognitive states, and that we should understand awareness as having a stronger causal role than confidence in perception and performance.

The pre-Bötzinger complex is a neural network located in the ventrolateral brainstem that generates the respiratory rhythm. Under normoxic conditions, this area shows two inspiratory burst patterns, sigh and non-sigh. Several studies have shown that in vitro application of peptides, such as bombesin, stimulates the respiratory rate and increases the appearance of sighs. However, it is difficult to distinguish between sighs and non-sighs waveforms, which makes it difficult to study their properties under experimental conditions. The fractal and multifractal analysis have proven to be valuable tools for studying physiological time series, thus in this study, we applied this methodology to characterize sighs and non-sighs. Our results regarding fractality, shown that the sighs and non-sighs have similar Hurst exponents and that the application of bombesin only decreased the Hurst exponent of non-sighs. On the other hand, our results on multifractality parameters scaling exponent ($\tau \left(q\right)$) and generalized Hurst exponent ($H\left(q\right)$) shown that both sighs and non-sighs were multifractal and this remained even after the application of bombesin. Further analysis showed that sighs and non-sighs had different $H\left(q\right)$ values, which changed after the bombesin application. To quantitatively analyzed the multifractal spectrum, we calculated the area of the spectrum ($I_{\alpha }$), which was similar between sighs and non-sighs and the application of bombesin did not change this. Altogether, these results show that the analysis of fractal and multifractal parameters allows to characterize and find statistical differences of sighs and non-sighs within and between different experimental conditions.

Statement of Significance

The characterization of the respiratory recordings is very difficult and time consuming when is done manually by a researcher. An automated software that can aid this can be very useful. Furthermore, this gives some parameters that can help to statistically differentiate between sighs and non sighs. One interesting finding was that multifractality show differences in the same condition between sighs and non sighs. Also, we found that the neuropeptide bombesin increases the number of sighs without changing the intrinsic structure of the respiration system. This is important to avoid the collapse of the lungs that can be incorporated in mechanical ventilators. We hope that you will find our paper suitable for publication in Brain Multiphysics and will look forward to receiving your response.

Given the complex nature of traumatic brain injury (TBI), assessment of injury risk directly from kinematic measures of head motion remains a challenge. Despite this challenge, kinematic-based measures of injury continue to be widely used to guide the design of protective equipment. In an effort to provide more insight into the relationship between rotational head kinematics and injury risk, we have conducted a large scale parametric finite element analysis (FEA) to investigate the role of angular acceleration, angular velocity, and angular jerk on the brain tissue strains and strain rates. The peak strains and strain rates resulting from rotational head accelerations were obtained for peak angular accelerations ranging from 0.5 - 25 krad/s${}^2$ and peak angular velocities ranging from 10 - 100 rad/s. The results of this study show that both angular acceleration and angular velocity have a significant effect on the peak tissue strains and strain rates, reinforcing the importance of accounting for both of these kinematic measures when evaluating injury risk. For a given magnitude of peak angular acceleration and angular velocity, increases in angular jerk are shown to have minimal effect on the peak tissue strains but can lead to an increase in the peak tissue strain rates. This advancement in our understanding of the relationship between angular head kinematics, tissue strain, and tissue strain rate is an important step toward developing improved kinematic-based measures of injury.

Statement of Significance

To reduce the risk of traumatic brain injury, we must first fully understand the relationship between impact-induced head motions and the brain deformation response. Large deformations of the brain have been shown to cause damage to neural cells and can result in long-term neurocognitive deficits. This study investigates the role of angular acceleration, angular velocity, and angular jerk on the tissue strains and strain rates that develop in the brain. By providing further insight into how each of these kinematic parameters affect the brain deformation response, we can begin to identify the types of head motions that are the most injurious and develop new targeted approaches to reduce the risk of injury.

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95^th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.

Statement of Significance

The study presents a method to calibrate and evaluate subject-specific finite element brain models using a combination of advanced magnetic resonance imaging (MRI) data. The imaging data is acquired in human volunteers and includes anatomical MRI, magnetic resonance elastography (MRE), and tagged MRI to generate subject-specific geometry, calibrate subject-specific material properties, and evaluate simulation response using subject-specific brain deformation. This dataset of MRE and tagged MRI allows for a unique evaluation of whether material properties from MRE can be used to create biofidelic computational models of the human brain. The study develops a calibration procedure to readily calculate linear viscoelastic material parameters from MRE data and then provides a sensitivity study of the effect of mechanical heterogeneity of the brain on simulation response. The calibrated computational models are used to simulate each subject's tagged MRI experiment; the results show good agreement between the simulated and experimental strain fields. The presented study and results will be informative in guiding the calibration of subject-specific computati