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Detection and discrimination of neurodegenerative Parkinson syndromes are challenging clinical tasks and the use of standard T1- and T2-weighted cerebral magnetic resonance (MR) imaging is limited to exclude symptomatic Parkinsonism. We used a quantitative structural MR-based technique, MR-elastography (MRE), to assess viscoelastic properties of th...
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The differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology and error rates in the clinical diagnosis can be high even at specialized centres. Despite several limitations, magnetic resonance imaging (MRI) has undoubtedly enhanced the diagnostic accuracy in the differential diagnosis of neurodegenerati...
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Introduction:
Neuroimaging play an increasingly important role in the diagnosis of parkinsonian syndromes.
Aim of the study:
In this paper, the authors elaborate on the necessity of using magnetic resonance imaging (MRI) in Parkinson's Disease (PD) and its potential role in differential diagnosis versus other neurodegenerative parkinsonian syndr...
Citations
... Brain stiffness can be measured noninvasively using acoustic vibrations that stimulate intracranial shear waves, as done in magnetic resonance elastography (MRE) [20,35,55,56]. Cerebral MRE [7,76] has been used to study changes in the mechanical consistency of the brain associated with both various physiological processes [27,57] and neurological disorders [15,38,47,65,67,75]. In MS patients, MRE revealed disseminated softening of the entire brain [75]. ...
Multiple sclerosis (MS) is a chronic neuroinflammatory disease that involves both white and gray matter. Although gray matter damage is a major contributor to disability in MS patients, conventional clinical magnetic resonance imaging (MRI) fails to accurately detect gray matter pathology and establish a clear correlation with clinical symptoms. Using magnetic resonance elastography (MRE), we previously reported global brain softening in MS and experimental autoimmune encephalomyelitis (EAE). However, it needs to be established if changes of the spatiotemporal patterns of brain tissue mechanics constitute a marker of neuroinflammation. Here, we use advanced multifrequency MRE with tomoelastography postprocessing to investigate longitudinal and regional inflammation-induced tissue changes in EAE and in a small group of MS patients. Surprisingly, we found reversible softening in synchrony with the EAE disease course predominantly in the cortex of the mouse brain. This cortical softening was associated neither with a shift of tissue water compartments as quantified by T2-mapping and diffusion-weighted MRI, nor with leukocyte infiltration as seen by histopathology. Instead, cortical softening correlated with transient structural remodeling of perineuronal nets (PNNs), which involved abnormal chondroitin sulfate expression and microgliosis. These mechanisms also appear to be critical in humans with MS, where tomoelastography for the first time demonstrated marked cortical softening. Taken together, our study shows that neuroinflammation (i) critically affects the integrity of PNNs in cortical brain tissue, in a reversible process that correlates with disease disability in EAE, (ii) reduces the mechanical integrity of brain tissue rather than leading to water accumulation, and (iii) shows similar spatial patterns in humans and mice. These results raise the prospect of leveraging MRE and quantitative MRI for MS staging and monitoring treatment in affected patients.
... The complex mechanical behavior of white matter tissue is attributable to the presence of stiff myelinated axons embedded within the soft extracellular matrix (ECM) [7][8][9][10] . It has been shown that finding localized stress, strain, or stiffness maps in white matter is of notable importance to many applications such as traumatic brain injury (TBI), diffusive axonal injury (DAI), and neurodegenerative brain disorders [11][12][13][14][15][16][17][18] . However, most experimental studies that employ classical tension, compression, shear tests, or magnetic resonance elastography (MRE) report the mechanical properties of the brain averaged over the gray and white matter within the macroscopic regions of interest 6,[19][20][21][22][23][24][25][26][27][28] . ...
... This is important as accurately determining the mechanical properties at a local level has the potential to enhance both sensitivity and specificity in diagnosing diseases, particularly because numerous neurological disorders often originate in specific localized areas or exhibit distinct regions of tissue damage. It has been shown that finding localized stiffness map in white matter is notably important for the study of neurodegenerative brain disorders [14][15][16][17] . For example, global or local reductions in brain stiffness have been reported in those afflicted with Parkinson's or Alzheimer's diseases has been reported 15,16,[76][77][78] . ...
... It has been shown that finding localized stiffness map in white matter is notably important for the study of neurodegenerative brain disorders [14][15][16][17] . For example, global or local reductions in brain stiffness have been reported in those afflicted with Parkinson's or Alzheimer's diseases has been reported 15,16,[76][77][78] . Currently, contemporary models mainly define the response of the composite bulk and homogenized white matter at macroscopic scales but fail to explicitly capture the connection between the independent material properties of microscopic constituents and bulk mechanical behavior. ...
Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new method to accurately map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element model of the fibrous tissue was subjected to six loading modes, and their corresponding stress-strain curves were characterized. By employing multiobjective optimization, an equivalent anisotropic material model was inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale finite element simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material model solely based on the fibrous architecture of any given tissue. The method was applied to imaging data of brain white matter tissue, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. The findings of this study have direct applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.
... The complex mechanical behavior of white matter tissue is attributable to the presence of stiff myelinated axons embedded within the soft extracellular matrix (ECM) [8][9][10][11] . It has been shown that finding localized stress, strain, or stiffness maps in white matter is of notable importance to many applications such as traumatic brain injury (TBI), diffusive axonal injury (DAI), and neurodegenerative brain disorders [12][13][14][15][16][17][18][19] . However, most experimental studies that employ classical tension, compression, shear tests report the mechanical properties of the brain averaged over the gray and white matter within the macroscopic regions of interest 20,21,7,[22][23][24][25][26][27] . ...
... This is important as accurately determining the mechanical properties at a local level has the potential to enhance both sensitivity and specificity in diagnosing diseases, given that numerous neurological disorders often originate in specific localized areas or exhibit distinct regions of tissue damage. It has been shown that finding localized stiffness map in white matter is notably important for the study of neurodegenerative brain disorders [15][16][17][18] . For example, global or local reductions in brain stiffness have been reported in those afflicted with Parkinson's or Alzheimer's diseases 16,17,30,86,87 . ...
... It has been shown that finding localized stiffness map in white matter is notably important for the study of neurodegenerative brain disorders [15][16][17][18] . For example, global or local reductions in brain stiffness have been reported in those afflicted with Parkinson's or Alzheimer's diseases 16,17,30,86,87 . ...
Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element model of the fibrous tissue was subjected to six loading cases, and their corresponding stress-strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale finite element simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The method was applied to local imaging data of brain white matter tissue, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.
... [27][28][29][30] MRE measures of brain tissue viscoelastic properties are strongly associated with normal aging [31][32][33][34][35] along with age-related neurodegenerative diseases such as AD [36][37][38] and Parkinson's disease. 39,40 Importantly, viscoelastic properties of the HC exhibit strong relationships with memory performance 41,42 and cardiometabolic risk factors 43 among healthy adults. As such, HC tissue viscoelastic properties may be better predictors of early declines in episodic memory than HC volume, which was confirmed in an observation made by a large meta-analysis showing the presence of weak associations between memory and size of the HC in healthy aging. ...
Arterial stiffness and cerebrovascular pulsatility are non-traditional risk factors of Alzheimer's disease. However, there is a gap in understanding the earliest mechanisms that link these vascular determinants to brain aging. Changes to mechanical tissue properties of the hippocampus (HC), a brain structure essential for memory encoding, may reflect the impact of vascular dysfunction on brain aging. We tested the hypothesis that arterial stiffness and cerebrovascular pulsatility are related to HC tissue properties in healthy adults across the lifespan. Twenty-five adults underwent measurements of brachial blood pressure (BP), large elastic artery stiffness, middle cerebral artery pulsatility index (MCAv PI), and magnetic resonance elastography (MRE), a sensitive measure of HC viscoelasticity. Individuals with higher carotid pulse pressure (PP) exhibited lower HC stiffness (β = -0.39, r = -0.41, p = 0.05), independent of age and sex. Collectively, carotid PP and MCAv PI significantly explained a large portion of the total variance in HC stiffness (adjusted R2 = 0.41, p = 0.005) in the absence of associations with HC volumes. These cross-sectional findings suggest that the earliest reductions in HC tissue properties are associated with alterations in vascular function.
... MRE is a phase-contrast magnetic resonance imaging (MRI) technique that can non-invasively measure in vivo tissue viscoelastic properties by measuring the threedimensional (3D) shear wave displacement field produced during the propagation of acoustic waves generated by an external mechanical vibration source [10,11]. Although its application has been studied extensively in the liver, MRE has also been applied to the brain, suggesting that numerous neurological conditions associated with glymphatic dysfunction (e.g., Alzheimer's disease, Parkinson's disease, normal pressure hydrocephalus, multiple sclerosis, and brain aging) exhibit significant findings on MRE [11][12][13][14][15][16][17][18]. ...
Objective:
To investigate the feasibility of assessing the viscoelastic properties of the brain using magnetic resonance elastography (MRE) and a novel MRE transducer to determine the relationship between the viscoelastic properties and glymphatic function in neurologically normal individuals.
Materials and methods:
This prospective study included 47 neurologically normal individuals aged 23-74 years (male-to-female ratio, 21:26). The MRE was acquired using a gravitational transducer based on a rotational eccentric mass as the driving system. The magnitude of the complex shear modulus |G*| and the phase angle ϕ were measured in the centrum semiovale area. To evaluate glymphatic function, the Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS) method was utilized and the ALPS index was calculated. Univariable and multivariable (variables with P < 0.2 from the univariable analysis) linear regression analyses were performed for |G*| and ϕ and included sex, age, normalized white matter hyperintensity (WMH) volume, brain parenchymal volume, and ALPS index as covariates.
Results:
In the univariable analysis for |G*|, age (P = 0.005), brain parenchymal volume (P = 0.152), normalized WMH volume (P = 0.011), and ALPS index (P = 0.005) were identified as candidates with P < 0.2. In the multivariable analysis, only the ALPS index was independently associated with |G*|, showing a positive relationship (β = 0.300, P = 0.029). For ϕ, normalized WMH volume (P = 0.128) and ALPS index (P = 0.015) were identified as candidates for multivariable analysis, and only the ALPS index was independently associated with ϕ (β = 0.057, P = 0.039).
Conclusion:
Brain MRE using a gravitational transducer is feasible in neurologically normal individuals over a wide age range. The significant correlation between the viscoelastic properties of the brain and glymphatic function suggests that a more organized or preserved microenvironment of the brain parenchyma is associated with a more unimpeded glymphatic fluid flow.
... These include improving the differential diagnosis of breast cancer (McKnight et al 2002, Sinkus et al 2007, Patel et al 2021, identifying tears in skeletal muscles, (Dresner et al 2001), detecting pulmonary disease in lungs (Mariappan et al 2014), and diagnosing prostate cancer (Brock et al 2015). MRE could also prove helpful in assessing the progression of Alzheimer's disease (Murphy et al 2011), Parkinson's disease (Lipp et al 2013), multiple sclerosis (Wuerfel et al 2010), brain integrity and microstructural changes in health and disease (Sack et al 2013), and evaluating normal pressure hydrocephalus (Streitberger et al 2011, Freimann et al 2012. Li and colleagues reviewed the crucial elements common to all successful magnetic resonance elastographic imaging systems (Li et al 2014): appropriate mechanical stimulation of the organ under investigation, acquiring wave images with a good signal-to-noise ratio (SNR), and computing robust estimates of shear modulus from the measured wave fields. ...
Reverberant elastography provides fast and robust estimates of shear modulus; however, its reliance on multiple mechanical drivers hampers clinical utility. In this work, we hypothesize that for constrained organs such as the brain, reverberant elastography can produce accurate magnetic resonance elastograms with a single mechanical driver. To corroborate this hypothesis, we performed studies on healthy volunteers (n=3); and a constrained calibrated brain phantom containing spherical inclusions with diameters ranging from 4-18 mm. In both studies (i.e., phantom and clinical), imaging was performed at frequencies of 50 Hz and 70 Hz. We used the accuracy and contrast-to-noise ratio performance metrics to evaluate reverberant elastograms relative to those computed using the established subzone inversion method. Errors incurred in reverberant elastograms varied from 1.3% to 16.6% when imaging at 50 Hz and 3.1% and 16.8 % when imaging at 70 Hz. In contrast, errors incurred in subzone elastograms ranged from 1.9% to 13% at 50 Hz and 3.6% to 14.9% at 70 Hz. The contrast-to-noise ratio of reverberant elastograms ranged from 63.1 dB to 73 dB compared to 65 dB to 66.2 dB for subzone elastograms. The average global brain stiffness estimated from reverberant and subzone elastograms was 2.36 ± 0.07 kPa and 2.38 ± 0.11 kPa, respectively, when imaging at 50 Hz and 2.70 ± 0.20 kPa and 2.89 ± 0.60 kPa respectively, when imaging at 70 Hz. The results of this investigation demonstrate that reverberant elastography can produce accurate, high-quality elastograms of the brain with a single mechanical driver.
... MRE has shown that the global brain softens because of aging at a rate of around 0.3-1% per year, with the specific softening rate varying among brain regions (Arani et al., 2015;Delgorio et al., 2021;Sack et al., 2011). The agerelated softening of the brain can be exacerbated by neurodegenerative disorders such as Alzheimer's disease, multiple sclerosis, and Parkinson's disease (Delgorio et al., 2023;Lipp et al., 2013;Murphy et al., 2011;Wuerfel et al., 2010). The sensitivity of mechanical property measures from MRE to tissue integrity make them strong candidates for imaging metrics used to predict brain health outcomes. ...
... For instance, stiffness measures have been shown to be sensitive to demyelination caused by multiple sclerosis; one study found a 13% decrease in cerebral viscoelasticity in MS patients compared to healthy volunteers (Wuerfel et al., 2010). Stiffness has also been shown to be sensitive to changes caused by Parkinson's disease, including a significant reduction in lentiform nucleus stiffness between PD patients and healthy, age-matched controls (Lipp et al., 2013). Alzheimer's disease has been most commonly (which was not certified by peer review) is the author/funder. ...
Brain age is a quantitative estimate to explain an individual's structural and functional brain measurements relative to the overall population and is particularly valuable in describing differences related to developmental or neurodegenerative pathology. Accurately inferring brain age from brain imaging data requires sophisticated models that capture the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a phase contrast MRI technology that uses external palpations to measure brain mechanical properties. Mechanical property measures of viscoelastic shear stiffness and damping ratio have been found to change across the entire life span and to reflect brain health due to neurodegenerative diseases and even individual differences in cognitive function. Here we develop and train a multi-modal 3D convolutional neural network (CNN) to model the relationship between age and whole brain mechanical properties. After training, the network maps the measurements and other inputs to a brain age prediction. We found high performance using the 3D maps of various mechanical properties to predict brain age. Stiffness maps alone were able to predict ages of the test group subjects with a mean absolute error (MAE) of 3.76 years, which is comparable to single inputs of damping ratio (MAE: 3.82) and outperforms single input of volume (MAE: 4.60). Combining stiffness and volume in a multi-modal approach performed the best, with an MAE of 3.60 years, whereas including damping ratio worsened model performance. Our results reflect previous MRE literature that had demonstrated that stiffness is more strongly related to chronological age than damping ratio. This machine learning model provides the first prediction of brain age from brain biomechanical data - an advancement towards sensitively describing brain integrity differences in individuals with neuropathology.
... In neuronal applications, MRE has been proven sensitive to disease and physiological effects both for 2D and 3D wave inversion (Hiscox et al., 2016;Yin et al., 2018;Murphy et al., 2019). Prominent examples include brain softening during aging (Sack et al., 2009;Arani et al., 2015;Hiscox et al., 2021) and Alzheimer's disease (Murphy et al., 2011;Murphy et al., 2016;Hiscox et al., 2020a), multiple sclerosis (Wuerfel et al., 2010;Fehlner et al., 2016), Parkinson's disease (Lipp et al., 2013;Lipp et al., 2018), and normal pressure hydrocephalus Freimann et al., 2012). Conversely, brain stiffening has been reported as a result of jugular compression (Hatt et al., 2015), Valsalva maneuver (Herthum et al., 2021a), hypercapnia (Hetzer et al., 2019), perfusion pressure (Hetzer et al., 2018), idiopathic intracranial hypertension (Kreft et al., 2020), and functional activation (Patz et al., 2019;Lan et al., 2020). ...
Purpose: Magnetic resonance elastography (MRE) generates quantitative maps of the mechanical properties of biological soft tissues. However, published values obtained by brain MRE vary largely and lack detail resolution, due to either true biological effects or technical challenges. We here introduce cerebral tomoelastography in two and three dimensions for improved data consistency and detail resolution while considering aging, brain parenchymal fraction (BPF), systolic blood pressure, and body mass index (BMI).
Methods: Multifrequency MRE with 2D- and 3D-tomoelastography postprocessing was applied to the brains of 31 volunteers (age range: 22—61 years) for analyzing the coefficient of variation (CV) and effects of biological factors. Eleven volunteers were rescanned after 1 day and 1 year to determine intraclass correlation coefficient (ICC) and identify possible long-term changes.
Results: White matter shear wave speed (SWS) was slightly higher in 2D-MRE (1.28 ± 0.02 m/s) than 3D-MRE (1.22 ± 0.05 m/s, p < 0.0001), with less variation after 1 day in 2D (0.33 ± 0.32%) than in 3D (0.96 ± 0.66%, p = 0.004), which was also reflected in a slightly lower CV and higher ICC in 2D (1.84%, 0.97 [0.88–0.99]) than in 3D (3.89%, 0.95 [0.76–0.99]). Remarkably, 3D-MRE was sensitive to a decrease in white matter SWS within only 1 year, whereas no change in white matter volume was observed during this follow-up period. Across volunteers, stiffness correlated with age and BPF, but not with blood pressure and BMI.
Conclusion: Cerebral tomoelastography provides high-resolution viscoelasticity maps with excellent consistency. Brain MRE in 2D shows less variation across volunteers in shorter scan times than 3D-MRE, while 3D-MRE appears to be more sensitive to subtle biological effects such as aging.
... In several human studies, researchers have reported that brain viscoelasticity is reduced during normal aging (31), Parkinson's disease (32), and Alzheimer's disease (33). Not surprisingly, there is a positive correlation between reduced brain stiffness and the clinical score in patients with MS. ...
Multiple sclerosis (MS) is a chronic inflammatory, demyelinating, and neurodegenerative disease in the central nervous system (CNS). Its pathogenesis is quite complex: Accumulated evidence suggests that biochemical signals as well as mechanical stimuli play important roles in MS. In both patients and animal models of MS, brain viscoelasticity is reduced during disease progression. Piezo mechanosensitive channels are recently discovered, and their three-dimensional structure has been solved. Both the membrane dome mechanism and the membrane footprint hypothesis have been proposed to explain their mechanosensitivity. While membrane-mediated forces alone appear to be sufficient to induce Piezo gating, tethers attached to the membrane or to the channel itself also seem to play a role. Current research indicates that Piezo1 channels play a key role in multiple aspects of MS pathogenesis. Activation of Piezo1 channels in axon negatively regulates CNS myelination. in addition, the inhibition of Piezo1 in CD4+ T cells and/or T regulatory cells (Treg) attenuates experimental autoimmune encephalitis (EAE) symptoms. Although more work has to be done to clarify the roles of Piezo1 channels in MS, they might be a promising future drug target for MS treatment.
... 2009 ;Lebel et al., 2012 ). Brain mechanical integrity is particularly susceptible to neurodegenerative disorders including Alzheimer's disease ( Murphy et al., 2011 ;Hiscox et al., 2020 ), Parkinson's disease ( Lipp et al., 2013 ;, and multiple sclerosis ( Wuerfel et al., 2010 ;Streitberger et al., 2012 ). Recent advancements in MRE imaging and image processing technology now allow brain MRE measures to be analyzed on a regional basis Daugherty et al., 2020 ), which has helped to provided more specific insights into aging and disease . ...
Magnetic resonance elastography (MRE) is a phase contrast MRI technique which uses external palpation to create maps of brain mechanical properties noninvasively and in vivo. These mechanical properties are sensitive to tissue microstructure and reflect tissue integrity. MRE has been used extensively to study aging and neurodegeneration, and to assess individual cognitive differences in adults, but little is known about mechanical properties of the pediatric brain. Here we use high-resolution MRE imaging in participants of ages ranging from childhood to adulthood to understand brain mechanical properties across brain maturation. We find that brain mechanical properties differ considerably between childhood and adulthood, and that neuroanatomical subregions have differing maturational trajectories. Overall, we observe lower brain stiffness and greater brain damping ratio with increasing age from 5 and 35 years. Gray and white matter change differently during maturation, with larger changes occurring in gray matter for both stiffness and damping ratio. We also found that subregions of cortical and subcortical gray matter change differently, with the caudate and thalamus changing the most with age in both stiffness and damping ratio, while cortical subregions have different relationships with age, even between neighboring regions. Understanding how brain mechanical properties mature using high-resolution MRE will allow for a deeper understanding of the neural substrates supporting brain function at this age and can inform future studies of atypical maturation.
