Brain multiphysics最新文献

Knowledge of the mechanical properties of brain tissue in vivo is essential to understanding the mechanisms underlying traumatic brain injury (TBI) and to creating accurate computational models of TBI and neurosurgical simulation. Brain white matter, which is composed of aligned, myelinated, axonal fibers, is structurally anisotropic. White matter in vivo also exhibits mechanical anisotropy, as measured by magnetic resonance elastography (MRE), but measurements of anisotropy obtained by mechanical testing of white matter ex vivo have been inconsistent. The minipig has a gyrencephalic brain with similar white matter and gray matter proportions to humans and therefore provides a relevant model for human brain mechanics. In this study, we compare estimates of anisotropic mechanical properties of the minipig brain obtained by identical, non-invasive methods in the live (in vivo) and dead animals (in situ). To do so, we combine wave displacement fields from MRE and fiber directions derived from diffusion tensor imaging (DTI) with a finite element-based, transversely-isotropic nonlinear inversion (TI-NLI) algorithm. Maps of anisotropic mechanical properties in the minipig brain were generated for each animal alive and at specific times post-mortem. These maps show that white matter is stiffer, more dissipative, and more anisotropic than gray matter when the minipig is alive, but that these differences largely disappear post-mortem, with the exception of tensile anisotropy. Overall, brain tissue becomes stiffer, less dissipative, and less mechanically anisotropic post-mortem. These findings emphasize the importance of testing brain tissue properties in vivo.

Statement of Significance

In this study, MRE and DTI in the minipig were combined to estimate, for the first time, anisotropic mechanical properties in the living brain and in the same brain after death. Significant differences were observed in the anisotropic behavior of brain tissue post-mortem. These results demonstrate the importance of measuring brain tissue properties in vivo as well as ex vivo, and provide new quantitative data for the development of computational models of brain biomechanics.

Understanding the correlation between pathological changes and the type of brain injury is pivotal in mitigating the damage and planning reliable and improved treatment strategies. Swift identification of the underlying mechanisms behind brain injury is essential for early diagnosis, surgical planning, and post-operative therapies. Brain injury may stem from various sources, including trauma (resulting in traumatic brain injury), treatment (leading to surgical brain injury), and neurodegenerative mechanisms. These injuries can manifest spatially, affecting individual neurons to the entire organ and temporally, ranging from immediate to long-term degeneration. However, direct evidence linking injury mechanisms to short and long-term tissue damage in the human population is limited, posing challenges in establishing a clear clinicopathological connection. Recently, in silico modeling has emerged as a cost-effective approach that can assist clinicians in gaining deeper insights and uncover new injury pathways. Physics and machine learning-based in silico modeling offers valuable contributions to injury prevention, diagnosis, prognosis, treatment planning, and patient monitoring, especially given the complexities of acquiring patient-specific clinical data related to brain injuries. Considering the spatiotemporal complexity of brain tissue damage, developing a comprehensive, multiscale, and multiphysics model is imperative for a better understanding. This study aims to categorize and explore strategies for modeling brain injuries across three distinct time scales, review damage mechanisms at various length scales, and recommend the development of a comprehensive biomechanical model that integrates multimodal data and multiphysics. Such an integrated approach will provide personalized diagnosis and treatment strategies tailored to individual patients.

Statement of Significance: The connection between clinical observations and brain pathology is crucial for managing brain injuries. Brain injuries result in brain damage via diverse factors across scales, from neurons to organs, from initial trauma to neurodegeneration. However, limited direct evidence linking injury mechanisms to long-term human tissue damage hinders clinicopathological connections. In silico modeling, a cost-effective approach utilizing physics and machine learning-based principles, can aid clinicians in uncovering injury pathways. A comprehensive, multimodal, and multiphysics model is vital for understanding complex brain tissue damage. This study categorizes modeling strategies, reviews damage mechanisms across scales, and recommends comprehensive biomechanical models for personalized treatment.

Background and purpose

Epilepsy is a complex neurological disorder affecting 50 million people worldwide. Persistent seizures may correlate with neural network, microstructural, and vascular changes within the thalamus. These thalamic changes may result from seizure activity or broader alterations involving neuronal vasculature and neuroinflammatory processes linked to glymphatic drainage. Improved resolution with Ultra-high field (UHF) magnetic resonance imaging (MRI) may be useful in identifying possible thalamic vascular abnormalities not otherwise detectable at lower field strengths.

Materials and methods

We outline a novel method which leverages UHF neuroimaging for detection and quantification of vessels and perivascular spaces (PVS) within the thalamus in 25 epilepsy patients and 16 controls, to uncover possible underlying imaging biomarkers of epilepsy. In our analysis, we optimize a MATLAB-based Frangi-based detection tool called Perivascular Space Semi-Automated Segmentation (PVSSAS) to detect thalamic PVSs, and additionally use a second Frangi-based segmentation tool method to automate detection of vascular structures in the thalamus. The resulting PVS and vessel masks were used to quantify differences in the number of vessels, PVS, overlaps, and number of PVS overlaps per vessel detected between groups, using a Hessian detection filter linked on an 18-connected network.

Results

We found significantly more thalamic PVS (p = 0.0307) and a significant increase in the number of thalamic vessels (p = 0.038) in patients compared to controls.

Conclusion

Here we have developed a novel process which leverages UHF MRI to quantify and detect thalamic vessels and PVS that may provide a potential neuroimaging biomarker of epilepsy.

Statement of Significance

We use 7T, ultra-high field MRI and employed an innovative combination of semi-automated perivascular space segmentation and automated vessel segmentation to visualize and quantify vessels and perivascular spaces (PVS) within the thalamus, a highly cited region of interest in epilepsy. To our knowledge, this is the first study to semi-automatically visualize and segment PVS in the thalamus and automatically detect thalamic vessels. We uncovered detectable differences in thalamic vasculature and PVS. These findings suggests that increases in the number of thalamic PVS and vessels may be a potential neuroimaging biomarker in epilepsy. This tool may be useful in the detection of subtle vascular changes in other regions of the brain related to epilepsy or can be employed in other neurological conditions.

Traumatic brain injury (TBI) is a major public health challenge. Up to 90 % of TBIs are on the mild spectrum of TBI (mTBI), where diagnosis is a major challenge. Majority of studies in this field have been conducted on human subjects, which inherently suffer from the lack of appropriate control group, selection bias, and individual differences in patients. To overcome these limitations, animal studies have been used as an alternative approach to provide deeper insights into the underlying mechanism related to the injury. Therefore our aim is to investigate various quantitative imaging biomarkers acquired from T1-W and diffusion tensor imaging (DTI) sequences to provide more information about the microstructural changes in the brain after mTBI. We then use this to generate subject-specific finite element models of the brain and examine how the changes in the brain material properties reflected in MR images affects strain distribution patterns on a subsequent head hit. Our study revealed a decrease in FA and an increase in diffusivity indices (MD, AD, RD) in the white matter tracts of the brain. This finding may represent the axonal damage, demyelination and gliosis after mild TBI, which have been shown in other animal and human studies. Moreover, our FE analysis showed that microstructural changes in the brain after mTBI might have weakened the structural integrity of the brain as the subsequent head hit led to wider and more severe brain deformations.

Significance

Animal models have been used to investigate biomechanical and pathophysiological aspects of mild traumatic brain injuries in the past. Still, most of them used small animals such as rats and mice. These models provided valuable insight into the pathophysiology of mTBI, but their findings have limitations due to their inherent differences to human brains. We have developed a large animal model of mTBI with sheep brains by combining advanced MRI and finite element analysis as they mimic the human brain better. To the best of our knowledge, this study is the first mTBI neuroimaging study conducted on large animal brains to investigate the diffusional changes in the white matter tracts after mTBI. Our FE analysis revealed that such microstructural changes resulted in tissue softening as the extent of brain deformation increased on a subsequent head hit, indicating increased brain vulnerability after head impacts.

Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of concussions. However, repetitive sub-concussive head impacts at the hyperacute phase (minutes after impact), are not understood. The head experiences rapid acceleration similar to a concussion, but without clinical symptoms. We utilize portable neuroimaging technology – transcranial Doppler (TCD) ultrasound and functional near infrared spectroscopy (fNIRS) – to estimate the extent of pre- and post-differences following contact and non-contact sparring sessions in nine MMA athletes. In addition, the extent of changes in neurofilament light (NfL) protein biomarker concentrations, and neurocognitive/balance parameters were determined following impacts. Athletes were instrumented with sensor-based mouth guards to record head kinematics. TCD and fNIRS results demonstrated significantly increased blood flow velocity (p = 0.01) as well as prefrontal (p = 0.01) and motor cortex (p = 0.04) oxygenation, only following the contact sparring sessions. This increase after contact was correlated with the cumulative angular acceleration experienced during impacts (p = 0.01). In addition, the NfL biomarker demonstrated positive correlations with angular acceleration (p = 0.03), and maximum principal and fiber strain (p = 0.01). On average athletes experienced 23.9 ± 2.9 g peak linear acceleration, 10.29 ± 1.1 rad/s peak angular velocity, and 1,502.3 ± 532.3 rad/s² angular acceleration. Balance parameters were significantly increased following contact sparring for medial-lateral (ML) center of mass (COM) sway, and ML ankle angle (p = 0.01), illustrating worsened balance. These combined results reveal significant changes in brain hemodynamics and neurophysiological parameters that occur immediately after sub-concussive impacts and suggest that the physical impact to the head plays an important role in these changes.

Statement of significance

: Brain injuries sustained during sport participation have received much attention since it is a common occurrence among participants. Although protective technologies have been developed over the years, the mechanism of injury is still unclear. There is less focus on the repetitive exposure to sub-concussive impacts on the functional integrity of the brain. Sub-concussive impacts are defined as a lesser impact force resulting in acceleration of the head without symptoms of concussion. Diminished neurocognitive performance has been associated with increased sparring exposure in amateur MMA/boxers suggesting that repeated sub-concussive blows may be just as harmful. However, no one has studied the potential effect of repeated sub-concussive head impacts at the hyperacute level defined as within minutes after impact. We apply novel mobile sensing tools such as head impact sensors and portable neuroimaging devices that allow us