Differential effect of dementia etiology on cortical stiffness as assessed by MR elastography

Pavuluri, KowsalyaDevi; Scott, Jonathan M.; Huston, John; Ehman, Richard L.; Manduca, Armando; Jack, Clifford R.; Savica, Rodolfo; Boeve, Bradley F.; Kantarci, Kejal; Petersen, Ronald C.; Knopman, David S.; Murphy, Matthew

doi:10.1016/j.nicl.2023.103328

Cited by 7 publications

(11 citation statements)

References 52 publications

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“…A good correlation was also found between storage modulus of these brain regions and more established AD biomarkers, such as hippocampal volume ( P = 0.0072) and amyloid PET ( P = 0.011), as well as with functional connectivity of the default mode network (DMN) ( P = 0.035). In addition, the same research group recently found a significant reduction of shear stiffness in the medial temporal region 78 . The finding of a reduction in global brain shear modulus magnitude in patients with AD has been corroborated by Gerischer et al who also reported finding a significant reduction in both shear modulus magnitude and phase angle for the hippocampus 76 .…”

Section: Mre Of Neurodegenerative Diseasementioning

confidence: 66%

“…In addition, the same research group recently found a significant reduction of shear stiffness in the medial temporal region. 78 The finding of a reduction in global brain shear modulus magnitude in patients with AD has been corroborated by Gerischer et al who also reported finding a significant reduction in both shear modulus magnitude and phase angle for the hippocampus. 76 Hiscox et al subsequently reported that the decrease in shear stiffness in patients with AD included white matter, cerebral cortex, and subcortical gray matter.…”

Section: Mre Of Neurodegenerative Diseasementioning

confidence: 68%

“…In animal studies, it has frequently been reported that the hippocampus 41,43,68 is affected and which is supported by one clinical study. A more recent clinical study showed the medial temporal region has the largest change of shear stiffness 78 . However, one animal study 69 and one clinical study 77 found no significant effects in the hippocampus.…”

Section: Discussion Of Challenges and New Perspectives For The Develo...mentioning

confidence: 99%

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Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Feng

Murphy

Hojo

et al. 2023

Magnetic Resonance Imaging

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Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) present a major health burden to society. Changes in brain structure and cognition are generally only observed at the late stage of the disease. Although advanced magnetic resonance imaging (MRI) techniques such as diffusion imaging may allow identification of biomarkers at earlier stages of neurodegeneration, early diagnosis is still challenging. Magnetic resonance elastography (MRE) is a noninvasive MRI technique for studying the mechanical properties of tissues by measuring the wave propagation induced in the tissues using a purpose‐built actuator. Here, we present a systematic review of preclinical and clinical studies in which MRE has been applied to study neurodegenerative diseases. Actuator systems for data acquisition, inversion algorithms for data analysis, and sample demographics are described and tissue stiffness measures obtained for the whole brain and internal structures are summarized. A total of six animal studies and eight human studies have been published. The animal studies refer to 123 experimental animals (68 AD and 55 PD) and 121 wild‐type animals, while the human studies refer to 142 patients with neurodegenerative disease (including 56 AD and 17 PD) and 166 controls. The animal studies are consistent in the reporting of decreased stiffness of the hippocampal region in AD mice. However, in terms of disease progression, although consistent decreases in either storage modulus or shear modulus magnitude are reported for whole brain, there is variation in the results reported for the hippocampal region. The clinical studies are consistent in reports of a significant decrease in either whole brain storage modulus or shear modulus magnitude, in both AD and PD and with different brain structures affected in different neurodegenerative diseases. MRE studies of neurodegenerative diseases are still in their infancy, and in future it will be interesting to investigate potential relationships between brain mechanical properties and clinical measures, which may help elucidate the mechanisms underlying onset and progression of neurodegenerative diseases.Evidence Level1.Technical EfficacyStage 2.

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Section: Mre Of Neurodegenerative Diseasementioning

confidence: 66%

Section: Mre Of Neurodegenerative Diseasementioning

confidence: 68%

Section: Discussion Of Challenges and New Perspectives For The Develo...mentioning

confidence: 99%

See 1 more Smart Citation

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Feng

Murphy

Hojo

et al. 2023

Magnetic Resonance Imaging

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“…Recent studies have shown that using stiffness of particular regions can serve as effective biomarkers for differentiating between Alzheimer's and other forms of dementia (ElSheikh et al, 2017;Pavuluri et al, 2023). This heightened softening in disease, particularly in patterns reflecting the distribution of pathology in the brain, point to the potential utility in a brain age model to identify and classify neurological conditions based on the difference with chronological age.…”

Section: Discussionmentioning

confidence: 99%

“…Alzheimer’s disease has been most commonly studied with MRE, and studies have shown lower stiffness in regions particularly affected by the disease (Gerischer et al, 2018; Hiscox, Johnson, McGarry, et al, 2020; Murphy et al, 2011, 2016). Recent studies have shown that using stiffness of particular regions can serve as effective biomarkers for differentiating between Alzheimer’s and other forms of dementia (ElSheikh et al, 2017; Pavuluri et al, 2023). This heightened softening in disease, particularly in patterns reflecting the distribution of pathology in the brain, point to the potential utility in a brain age model to identify and classify neurological conditions based on the difference with chronological age.…”

Section: Discussionmentioning

confidence: 99%

Mechanical Property Based Brain Age Prediction using Convolutional Neural Networks

Clements

Claros-Olivares

McIlvain

et al. 2023

Preprint

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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.

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A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding

Wang,

Chen,

Yan

et al. 2024

Magnetic Resonance in Med

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PurposesTo enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts.MethodsA displacement‐encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center‐out golden‐angle stack‐of‐stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel‐imaging and compressed‐sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE–based MRE sequences via phantom and brain acquisitions.ResultsThe proposed sequence achieved a 6‐fold acceleration compared with GRE MRE. With the application of a conventional parallel‐imaging and compressed‐sensing algorithm, the scanning speed was further accelerated by 8‐fold, matching the speed of EPI‐based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast‐to‐noise ratio (1.38) than the two EPI‐based sequences (0.61/0.76) and similar to GRE‐based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance.ConclusionA rapid DENSE‐based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI‐based sequences, without compromising SNR or introducing distortion artifacts.

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Differential effect of dementia etiology on cortical stiffness as assessed by MR elastography

Cited by 7 publications

References 52 publications

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Mechanical Property Based Brain Age Prediction using Convolutional Neural Networks

A 3D fast MR elastography sequence with interleaved multislab acquisition and Hadamard encoding

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