Magnetic Resonance Imaging in Animal Models of Alzheimer’s Disease Amyloidosis

Ni, Ruiqing

doi:10.3390/ijms222312768

Cited by 24 publications

(17 citation statements)

References 265 publications

(269 reference statements)

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“…On the other hand, biomarkers based on diagnostic imaging methods provide information about histological abnormalities compatible with AD. Among these markers are positron tomography (PET) and high-resolution magnetic resonance imaging (MRI) [ 85 , 86 , 87 , 88 ]. PET is a diagnostic method that elucidates the processes involved in cellular metabolism and can map the specific proteins involved in the disease.…”

Section: Resultsmentioning

confidence: 99%

Current Understanding of the Physiopathology, Diagnosis and Therapeutic Approach to Alzheimer’s Disease

et al. 2021

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Alzheimer’s disease (AD) is the most common cause of dementia. It is characterized by cognitive decline and progressive memory loss. The aim of this review was to update the state of knowledge on the pathophysiological mechanisms, diagnostic methods and therapeutic approach to AD. Currently, the amyloid cascade hypothesis remains the leading theory in the pathophysiology of AD. This hypothesis states that amyloid-β (Aβ) deposition triggers a chemical cascade of events leading to the development of AD dementia. The antemortem diagnosis of AD is still based on clinical parameters. Diagnostic procedures in AD include fluid-based biomarkers such as those present in cerebrospinal fluid and plasma or diagnostic imaging methods. Currently, the therapeutic armory available focuses on symptom control and is based on four pillars: pharmacological treatment where acetylcholinesterase inhibitors stand out; pharmacological treatment under investigation which includes drugs focused on the control of Aβ pathology and tau hyperphosphorylation; treatment focusing on risk factors such as diabetes; or nonpharmacological treatments aimed at preventing development of the disease or treating symptoms through occupational therapy or psychological help. AD remains a largely unknown disease. Further research is needed to identify new biomarkers and therapies that can prevent progression of the pathology.

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Section: Resultsmentioning

confidence: 99%

Current Understanding of the Physiopathology, Diagnosis and Therapeutic Approach to Alzheimer’s Disease

et al. 2021

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“…For clinical application in humans, 7 and 10.5T MRIs have been reported ( Ehman et al, 2017 ; Ladd et al, 2018 ). For small animal imaging, 7, 9.4, 11.7, 16, and up to 21.1 T high-field MRI has been utilized in the laboratory ( Schepkin et al, 2010 ; Miyaoka and Lehnert, 2020 ; Ni, 2021 ), providing insights into the function and pathophysiology of the brain. Higher magnetic fields substantially increase the sensitivity and signal-to-noise ratio for MRI, although the tissue heating and non-uniformity of the radio-frequency field might affect the image quality.…”

Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging in Tauopathy Animal Models

2022

Front. Aging Neurosci.

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The microtubule-associated protein tau plays an important role in tauopathic diseases such as Alzheimer’s disease and primary tauopathies such as progressive supranuclear palsy and corticobasal degeneration. Tauopathy animal models, such as transgenic, knock-in mouse and rat models, recapitulating tauopathy have facilitated the understanding of disease mechanisms. Aberrant accumulation of hyperphosphorylated tau contributes to synaptic deficits, neuroinflammation, and neurodegeneration, leading to cognitive impairment in animal models. Recent advances in molecular imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI) have provided valuable insights into the time course of disease pathophysiology in tauopathy animal models. High-field MRI has been applied for in vivo imaging in animal models of tauopathy, including diffusion tensor imaging for white matter integrity, arterial spin labeling for cerebral blood flow, resting-state functional MRI for functional connectivity, volumetric MRI for neurodegeneration, and MR spectroscopy. In addition, MR contrast agents for non-invasive imaging of tau have been developed recently. Many preclinical MRI indicators offer excellent translational value and provide a blueprint for clinical MRI in the brains of patients with tauopathies. In this review, we summarized the recent advances in using MRI to visualize the pathophysiology of tauopathy in small animals. We discussed the outstanding challenges in brain imaging using MRI in small animals and propose a future outlook for visualizing tau-related alterations in the brains of animal models.

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“…To train supervised models, features are generally extracted from task-based images; however, such models require human specialists, as well as ample effort, time, and funding (14). This presents an enormous challenge for the continuation of this mode of disease diagnosis (15,16). With the emergence of deep-learning (DL) models, it is feasible to retrieve features directly from imaging data without human intervention.…”

Section: Introductionmentioning

confidence: 99%

“…The relative ease of this approach is compelling more research into DL models to enable the precise diagnosis of various diseases (17,18). Compared to the challenges of other image analysis programs [e.g., computed tomography (CT), MRI, X-rays, ultrasounds, and sentiment analysis], DL models have achieved considerable success (16,19). Notably, they have been reported to produce reliable results in terms of disease diagnosis and stratification, especially in the lungs, abdomen, brain, cardiovascular, and retina.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic accuracy study of automated stratification of Alzheimer’s disease and mild cognitive impairment via deep learning based on MRI

Chen¹,

Tang²,

Liu³

et al. 2022

Ann Transl Med

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Background: Alzheimer's disease (AD) is a widespread neurodegenerative disease that mostly affects the elderly population. Given its prevalence, a precise and efficient stratification system based on AD symptomology that uses functional magnetic resonance imaging (MRI) has great potential in the clinical diagnosis and prognosis estimation of AD patients. It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability. Methods: We proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI, and NCs) process using MRI. And the accuracy, recall, specificity, area under the curve of receiver operating characteristic curve (AUC), F1 and Matthew's correlation coefficient (MCC) scores to assess accuracy of auxiliary clinical diagnoses.Results: Compared to other combinations of algorithms, our model obtained remarkable overall stratification accuracies in all different classification sets. In terms of AD vs. MCI, the mean training AUC of the 3 runs were 85.1% in 95% confidence intervals (CIs). In terms of AD vs. NC, the mean training AUC of the 3 runs was 90.6% in 95% CIs. In terms of the 3 stratifications of AD, MCI, and NC, relative precision, recall, and specificity for each category in the training test (TS) were all near 89%, while the F1 and MCC scores of both sets were 59.9% and 59.5%, respectively.Conclusions: Using a deep CNN and iterated RF architecture, we showed that brain image stratification is a promising means for evaluating AD, and examining the underlying etiology of the disease, by applying computer and medical images to achieve the early auxiliary diagnosis of AD. However, we still have a long way to go from the discovery of image markers to clinical application.

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Magnetic Resonance Imaging in Animal Models of Alzheimer’s Disease Amyloidosis

Cited by 24 publications

References 265 publications

Current Understanding of the Physiopathology, Diagnosis and Therapeutic Approach to Alzheimer’s Disease

Current Understanding of the Physiopathology, Diagnosis and Therapeutic Approach to Alzheimer’s Disease

Magnetic Resonance Imaging in Tauopathy Animal Models

Diagnostic accuracy study of automated stratification of Alzheimer’s disease and mild cognitive impairment via deep learning based on MRI

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