Traumatic brain injury (TBI) is a worldwide health issue. Increasing prevalence of blast-induced TBI (bTBI), a predominantly combat-related injury, is an alarming trend necessitating a better understanding of the associated pathogenesis to develop treatments. Further, most bTBI injuries are mild and undiagnosed, permitting secondary biochemical injuries to propagate beyond possible intervention. Unfortunately, few treatment options are available due to a limited understanding of the underlying mechanisms. Additional investigative tools are urgently needed to elucidate the mechanisms behind immediate and long-term bTBI-induced damage. Therefore, we introduce “bTBI-on-a-Chip,” an in vitro blast injury model, capable of simultaneous morphological, biochemical, and bioelectrical assessments before, during, and after blast injury. We show correlated increases in markers of oxidative stress (acrolein) and inflammation (TNF-α) accompanied by electrophysiological deficits post-blast injury. Additionally, we show that these pathological consequences are mitigated by acrolein scavenging. We also show that injury products released by cultures post-injury diffuse through culture media and instigate biochemical injury in uninjured neuronal networks. Furthermore, we show that acrolein, a diffusive component of post-TBI secondary injury, is sufficient to increase inflammation in uninjured cultures. These findings validate bTBI-on-a-Chip as an appropriate model for recapitulating and investigating blast injury in vitro by showing its capabilities of recreating primary and secondary bTBI, monitoring biochemical and electrophysiological responses to injury, and screening possible pharmacological interventions post-injury. We expect that this model could provide insights into the pathological biochemical mechanisms that will be critical in developing future diagnostic and treatment strategies for bTBI patients. Statement of Significance: The findings in the current study validate bTBI-on-a-Chip as an appropriate model for recapitulating and investigating blast injury in vitro by demonstrating its capabilities of recreating primary and secondary bTBI, monitoring biochemical and electrophysiological responses to injury, and screening possible pharmacological interventions post-injury. We expect that this model could provide insights into the pathological biochemical mechanisms that will be critical in developing future diagnostic and treatment strategies for bTBI patients.
Proper scaling is an important concept in physics. It allows theoretical frameworks originally developed to address a specific question to be generalized or recycled to solve another problem at a different scale. The rescaling of the theory of heat to link diffusion and Brownian motion is a famous example set out by Einstein. We have recently shown how the special and general relativity theories could be scaled down to the action potential propagation speed in the brain to explain some of its functioning: Functional “distances” between neural nodes (geodesics), depend on both the spatial distances between nodes and the time to propagate between them, through a connectome spacetime with four intricated dimensions. This spacetime may further be curved by neural activity suggesting how conscious activity could act in a similar the gravitational field curved the physical spacetime. Indeed, the apparent gap between the microscopic and macroscopic connectome scales may find an echo in the AdS/CFT correspondence. Applied to the brain connectome, this means that consciousness may appear as the emergence in a 5D spacetime of the neural activity present as its boundaries, the 4D cortical spacetime, as a holographic 5D construction by our inner brain. We explore here how the conflict between ‘consciousness and matter’ could be resolved by considering that the spacetime of our cerebral connectome has five dimensions, the fifth dimension allowing the natural, immaterial emergence of consciousness as a dual form of the 4D spacetime embedded in our material cerebral cortex. Statement Of Significance Scaling to the brain the AdS/CFT framework which shows how the General Gravity framework, hence gravitation, naturally (mathematically) emerges from a “flat”, gravitationless Quantum 4D spacetime once a fifth dimension is considered, we conjecture that the conflict between ‘consciousness and matter’ might be ill-posed and could be resolved by considering that the spacetime of our cerebral connectome has five dimensions, the fifth dimension allowing the natural, immaterial (mind, private) emergence of consciousness as a dual form of the 4D spacetime activity embedded in our material (body, public) cerebral cortex.
Background and purpose Idiopathic normal pressure hydrocephalus (iNPH) is a cerebrospinal fluid (CSF) dynamics disorder as evidenced by the delayed ascent of radiotracers over the cerebral convexity on radionuclide cisternography. However, the exact mechanism causing this disruption remains unclear. Elucidating the pathophysiology of iNPH is crucial, as it is a treatable cause of dementia. Improving the diagnosis and treatment prognosis rely on the better understanding of this disease. In this study, we calculated the pulsatile transmantle pressure and investigated the phase lag between this pressure and the volumetric CSF flow rate as a novel biomarker of CSF dynamics disruption in iNPH. Methods 44 iNPH patients and 44 age- and sex-matched cognitively unimpaired (CU) control participants underwent MRI scans on a 3T Siemens scanner. Pulsatile transmantle pressure was calculated analytically and computationally using volumetric CSF flow rate, cardiac frequency, and aqueduct dimensions as inputs. CSF flow rate through the aqueduct was acquired using phase-contrast MRI. The aqueduct length and radius were measured using 3D T1-weighted anatomical images. Results Peak pressure amplitudes and the pressure load (integrated pressure exerted over a cardiac cycle) were similar between the groups, but the non-dimensionalized pressure load (adjusted for anatomical factors) was significantly lower in the iNPH group (p<0.001, Welch's t-test). The phase lag between the pressure and the flow rate, arising due to viscous drag, was significantly higher in the iNPH group (p<0.001). Conclusion The increased phase lag is a promising new biomarker for quantifying CSF dynamics dysfunction in iNPH. Statement of Significance The exact mechanism causing the disruption of CSF circulation in idiopathic normal pressure hydrocephalus (iNPH) remains unclear. Elucidating the pathophysiology of iNPH is crucial, as it is a treatable cause of dementia. In this study, we provided an analytical and a computational method to calculate the pulsatile transmantle pressure and the phase lag between the pressure and the volumetric CSF flow rate across the cerebral aqueduct. The phase lag was significantly higher in iNPH patients than in controls and may serve as a novel biomarker of CSF dynamics disruption in iNPH.
Multiple Sclerosis (MS) is an autoimmune condition leading to the degeneration of brain tissue, occurring when the immune system attacks the myelin sheath surrounding axons of white brain matter thereby disrupting brain signals. This study aimed to evaluate how MS lesions alter stress distribution through grey and white brain matter with lesions (active, chronic, and inactive). A linear viscoelastic model represents the tissue-scale dynamic deformation and time dependency of brain tissue. A Prony series expansion was used to model viscous effects including stress relaxation. An elastic modulus, within the viscoelastic model, was either reduced by 11 % for active lesions, or increased by 35 % increase for inactive lesions. These material properties were then implemented to model healthy tissue, active, chronically inflamed, and inactive lesions. Finite element analysis enabled stress evaluation in response to a peak cyclic displacement of 0.5 mm (1 % strain) with the healthy model acting as a control model. Chronic lesions had the largest effect on stress induced, in terms of high (171 Pa) and low stress (108 Pa). Inactive lesions induced an increase in stress of 11 Pa with areas of low stress (105 Pa). Active lesions caused the least deviation in peak induced stress (7 Pa). In conclusion, a hierarchy in stress induced across the lesion types has been found, from highest to lowest: chronic, inactive and active, with potential implications for lesion progression. In conclusion, MS lesions within brain tissue should model lesions, avoid assuming homogeneity during degeneration, and should distinguish between active and passive lesions.
Alzheimer’s disease is a neurodegenerative disorder characterized by the presence of amyloid-β plaques and the accumulation of misfolded tau proteins and neurofibrillary tangles in the brain. A thorough understanding of the local accumulation of tau is critical to develop effective therapeutic strategies. Tau pathology has traditionally been described using reaction–diffusion models, which succeed in capturing the global spread, but fail to accurately describe the local aggregation dynamics. Current mathematical models enforce a single-peak behavior in tau aggregation, which does not align well with clinical observations. Here we identify a more accurate description of tau aggregation that reflects the complex patterns observed in patients. We propose an innovative approach that uses constitutive neural networks to autonomously discover bell-shaped aggregation functions with multiple peaks from clinical positron emission tomography (PET) data of misfolded tau protein. Our method reveals previously overlooked two-stage aggregation dynamics by uncovering a two-term ordinary differential equation that links the local accumulation rate to the tau concentration. When trained on data from amyloid-β positive and negative subjects, the neural network clearly distinguishes between both groups and uncovers a more subtle relationship between amyloid-β and tau than previously postulated. In line with the amyloid–tau dual pathway hypothesis, our results show that the presence of toxic amyloid-β influences the accumulation of tau, particularly in the earlier disease stages. We expect that our approach to autonomously discover the accumulation dynamics of pathological proteins will improve simulations of tau dynamics in Alzheimer’s disease and provide new insights into disease progression.Significance StatementIn Alzheimer’s disease, understanding the local dynamics of tau protein aggregation is crucial for developing effective treatments. Traditional models for tau protein dynamics use reaction–diffusion models that fail to accurately capture these local patterns. Our study introduces a novel approach that leverages constitutive neural networks to autonomously discover the complex, multi-peak aggregation dynamics from clinical PET data. This method reveals a previously overlooked two-stage tau accumulation process and a nuanced relationship between amyloid-β and tau. By distinguishing between amyloid-β positive and negative subjects, our model supports the amyloid–tau dual pathway hypothesis and offers novel insights into tau protein aggregation that have the potent to advance our understanding of Alzheimer’s disease progression.
Delivery of drug using nanocarriers tethered with vasculature-targeting epitopes aims to maximize the therapeutic efficacy of the drug while minimizing the drug side effects. Circadian rhythm which is governed by the central nervous system has implications for targeted drug delivery due to sleep-wake cycle changes in blood flow dynamics. This paper presents an advanced fluid dynamics modeling method that is based on viscous incompressible shear-rate fluid (blood) coupled with an advection-diffusion equation to simulate the formation of drug concentration gradients in the blood stream and buildup of concentration at the targeted site. The method is equipped with an experimentally calibrated nanoparticle-endothelial cell adhesion model that employs Robin boundary conditions to describe nanoparticle retention based on probability of adhesion, a friction model accounting for surface roughness of endothelial cell layer, and a dispersion model based on Taylor-Aris expression for effective diffusion in the boundary layer. The computational model is first experimentally validated and then tested on engineered bifurcating arterial systems where impedance boundary conditions are applied at the outflow to account for the downstream resistance at each outlet. It is then applied to a virtual geometric model of an in vivo arterial tree developed through MRI-based image processing techniques. These simulations highlight the potential of the computational model for drug transport, adhesion, and retention at multiple sites in virtual in vivo models. The model provides a virtual platform for exploring circadian rhythm modulated blood flow for targeted drug delivery while minimizing the in vivo experimentation. Statement of Significance:A novel integration of nanoparticle-based drug delivery framework with shear-rate dependent blood flow model is presented. The framework is comprised of a unique combination of mechanics-based dispersion model, an asperity model for endothelium surface roughness, and a stochastic nanoparticle-endothelial cell adhesion model. Simulations of MRI based in vivo carotid artery system showcase the effects of vessel geometry on nanoparticle adhesion and retention at the targeted site. Vessel geometry and target site location impact nanoparticle adhesion; curved and bifurcating regions favor local accumulation of drug. It is also shown that aligning drug administration with circadian rhythm and sleep cycle can enhance the efficacy of drug delivery processes. These simulations highlight the potential of the computational modeling for exploring circadian rhythm modulated blood flow for targeted drug delivery while minimizing the in vivo experimentation.
Ski racing is a high-risk sport for traumatic brain injury. A better understanding of the injury mechanism and the development of effective protective equipment remains central to resolving this urgency. Finite element (FE) models are useful tools for studying biomechanical responses of the brain, especially in real-world ski accidents. However, real-world accidents are often captured by handheld monocular cameras; the videos are shaky and lack depth information, making it difficult to estimate reliable impact velocities and posture which are critical for injury prediction. Introducing novel computer vision and deep learning algorithms offers an opportunity to tackle this challenge. This study proposes a novel framework for estimating impact kinematics from handheld, shaky monocular videos of accidents to inform personalized impact simulations. The utility of this framework is demonstrated by reconstructing a ski accident, in which the extracted kinematics are input to a neuroimaging-informed, personalized FE model. The FE-derived responses are compared with imaging-identified brain injury sites of the victim. The results suggest that maximum principal strain may be a useful metric for brain injury. This study demonstrates the potential of video-informed accident reconstructions combined with personalized FE modeling to evaluate individual brain injury.
Throughout the 19th and 20th centuries, aided by advances in medical imaging, discoveries in physiology and medicine have added nearly 25 years to the average life expectancy. This resounding success brings with it a need to understand a broad range of age-related health conditions, such as dementia. Today, mathematics, neuroimaging and scientific computing are being combined with fresh insights, from animal models, to study the brain and to better understand the etiology and progression of Alzheimer’s disease, the most common cause of age-related dementia in humans. In this manuscript, we offer a brief primer to the reader interested in engaging with the exciting field of mathematical modeling and scientific computing to advance the study of the brain and, in particular, human AD research.Statement of SignificanceModeling Alzheimer’s disease is a highly interdisciplinary field and finding an effective starting point can be a considerable challenge. To address this challenge, this manuscript briefly highlights some central components of AD related protein pathology, useful classes of mathematical models for brain and AD research and effective computational resources for the practical prospective practitioner.
Alzheimer's disease is the most common dementia worldwide. Its pathological development is well known to be connected with the accumulation of two toxic proteins: tau protein and amyloid-$\beta$. Mathematical models and numerical simulations can predict the spreading patterns of misfolded proteins in this context. However, the calibration of the model parameters plays a crucial role in the final solution. In this work, we perform a sensitivity analysis of heterodimer and Fisher-Kolmogorov models to evaluate the impact of the equilibrium values of protein concentration on the solution patterns. We adopt advanced numerical methods such as the IMEX-DG method to accurately describe the propagating fronts in the propagation phenomena in a polygonal mesh of sagittal patient-specific brain geometry derived from magnetic resonance images. We calibrate the model parameters using biological measurements in the brain cortex for the tau protein and the amyloid-$\beta$ in Alzheimer's patients and controls. Finally, using the sensitivity analysis results, we discuss the applicability of both models in the correct simulation of the spreading of the two proteins.
Traumatic Brain Injury (TBI) represents a major public health burden and a major contributor to disability and death, especially in the young population. It remains one of the most challenging human conditions to classify and the non-standardized classification is one of the numerous barriers in proper diagnosis and effective translation of experimental treatments in animal models. TBI is associated with numerous disorders, including amnesia, Parkinson's Disease, sleep disorders, Alzheimer's Disease, as well as disruption of physical, cognitive, and mental functioning. Several health care providers and the insurance industry see TBI as a singular 'event', meaning that the brain ''repairs'' over time, and does not require additional therapies. However, a single mild TBI can induce problems that self-propagate for months or years after the injury. There currently exist no diagnostic methods to quantify the extent of emotional and behavioral changes, cognitive impairment, fatigue, and sleep issues resulting from TBI in affected individual. The various animal and injury models available for TBI research are limited in clinical trials because a single TBI event is not fully understood. This review highlights the classifications of TBI, its heterogeneity, neuropathological lesions, long term sequelae, association with neurodegenerative disorders in human and animal studies, and attempts to modify the notion of TBI being viewed as a singular event.STATEMENT OF SIGNIFICANCE: The significance and strength of this review article lies in its comprehensive exploration of Traumatic Brain Injury (TBI) by addressing various factors that contribute to its complexity. We carried out a careful and detailed review of TBI classifications with an aim to provide a clearer and more detailed understanding of the heterogeneity inherent in these injuries. The examination of neuropathological lesions associated with TBI offers critical insights into the intricate nature of brain damage, fostering a deeper comprehension of the diverse outcomes resulting from TBI.Furthermore, this review critically evaluates the long-term sequelae of TBI, shedding light on the often-overlooked extended consequences that impact individuals well beyond the initial injury period. The findings from human and animal studies not only enriches our understanding of TBI but also highlights the translational implications for both clinical and preclinical research.A pivotal aspect of our review involves investigating the association between TBI and neurodegenerative disorders. By combining information from human studies and animal models, we aim to contribute to the growing body of knowledge that elucidates the intricate links between TBI and the development of neurodegenerative conditions.Most notably, this review challenges the conventional notion of TBI as a singular event by incorporating perspectives that emphasize its multifaceted nature. We critically assess attempts to modify this prevailing paradigm, encouraging a more nuanced understanding that considers the diverse manifestations and outcomes associated with TBI.In essence, this review endeavours to serve as a valuable resource for researchers, clinicians, and policymakers, fostering a comprehensive understanding of TBI that transcends traditional classifications and contributes to a more holistic approach in both research and clinical practice.
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.
The human brain has a complex and unique structure, characterized by intricate three-dimensional folds. These folds, and the mechanisms for their formation, have been studied for over a hundred years. Here we offer a full translation of the pivotal (1929) work by Siegfried Bok, “Der Einflußder in den Furchen und Windungen auftretenden Krümmungen der Großhirnrinde auf die Rindenarchitektur” (“The Influence of the Curvature Occurring in the Folds and Turns of the Cerebral Cortex on Cortical Architecture”). This paper established the influential equi-volume principle, which stated that cortical and laminar thicknesses, along with neuronal shape and fiber orientation, change in order to preserve relative volume throughout the folds of the cortex. We also offer a commentary on the main points of the work, looking at Bok’s observations and predictions regarding the structure of neurons, cortical laminae, and the cortex itself, throughout the folds and curves of the brain. His equi-volume principle has held up to decades of experimentation and, even today, has important implications for the analysis of brain structure and function.
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.
Alzheimer's disease (AD) is suggested to be a heterogeneous disorder, but limited studies explore the heterogeneity of the Mild Cognitive Impairment (MCI) stage. This study aimed to tackle such problems using the CIMLR (Cancer Integration via Multikernel Learning) algorithm to cluster brain structural features extracted from T1-weighted Magnetic Resonance Images of MCI patients from Alzheimer's Disease Neuroimaging Initiative. The demographic and cognitive results, characteristics of brain structural features, plasma biomarkers, and longitudinal cognitive trajectory were analyzed for each cluster. The CIMLR clustering analysis revealed four distinct clusters. Cluster 1 is the oldest group but has had mild atrophy and moderate progression with elevated Tumor Necrosis Factor Receptor 2 level; and low Brain-Derived Neurotrophic Factor and CD40 Ligand levels. Cluster 2 showed the highest risk for aggressive MCI progression, with abnormal Leptin, Adiponectin, and Creatine kinase-MB values. Cluster 3 exhibited a low level of Monokine Induced by Gamma Interferon and mild atrophy that shared similar patterns with Cluster 1. Cluster 4 represented the healthiest group during longitudinal tracking, with the mildest Parahippocampal atrophy, which was found to be positively correlated with cognitive impairment and amino acid levels. The longitudinal analyses showed the potential of Hepatocyte Growth Factor as a marker for slow cognitive impairment; Cortisol and Neurofilament Light Polypeptide as prognosis markers for aggressive MCI progression. These findings may lay out new suggestions for further research contributing to the accurate diagnosis and precision medicine for dementia and AD.
Chiari Malformation type 1 (CM-I) is a neurological disorder characterized by morphological defects including excessive cerebellar tonsillar ectopia and associated manifestations. We used 3D amplified MRI on a cohort of healthy and CM-I subjects to investigate the brain’s intrinsic motion, its association with the morphology and patient’s symptomatology, and surgical outcomes. We observed that the regional brain motion in CM-I was significantly higher than that of the healthy subjects, with anterior-posterior (AP) and superior-inferior (SI) displacements in cerebellar tonsils and medulla having the highest differences between the healthy and CM-I (∼45%–∼73% increased motion in the CM-I group). Interestingly, we found the ratio of neural tissue in the foramen magnum to be directly correlated with the SI tonsillar motion (r=0.58). Tonsillar herniation was directly correlated with the AP motion of the tonsils (r=0.61), and AP and medial-lateral (ML) motions of the medulla (r=0.66, and r=0.57). Subjects with higher tonsillar ML motion prior to surgery showed improved outcome (p=0.03, and AUC=0.95). Although we did not observe a significant correlation between the brains motion and morphometrics on the CM-I symptoms (perhaps due to our small sample size), illustrative cases increase our hope for the development of a future tool based on brain biomechanics.
