scholarly journals High field magnetic resonance microscopy of the human hippocampus in Alzheimer's disease: Quantitative imaging and correlation with iron

NeuroImage ◽  
2012 ◽  
Vol 59 (2) ◽  
pp. 1249-1260 ◽  
Author(s):  
Vijay Antharam ◽  
Joanna F. Collingwood ◽  
John-Paul Bullivant ◽  
Mark R. Davidson ◽  
Saurav Chandra ◽  
...  
2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Alexa Haeger ◽  
Arthur Coste ◽  
Cécile Lerman‐Rabrait ◽  
Julien Lagarde ◽  
Jörg B. Schulz ◽  
...  

NeuroImage ◽  
1998 ◽  
Vol 7 (4) ◽  
pp. S519
Author(s):  
Helene Benveniste ◽  
Gillian Einstein ◽  
Katie R. Kim ◽  
G. Allan Johnson

2014 ◽  
Vol 11 (4) ◽  
pp. 367-388 ◽  
Author(s):  
Ningnannan Zhang ◽  
Xiaowei Song ◽  
Robert Bartha ◽  
Steven Beyea ◽  
Ryan D’Arcy ◽  
...  

1999 ◽  
Vol 96 (24) ◽  
pp. 14079-14084 ◽  
Author(s):  
H. Benveniste ◽  
G. Einstein ◽  
K. R. Kim ◽  
C. Hulette ◽  
G. A. Johnson

2021 ◽  
Vol 22 (23) ◽  
pp. 12768
Author(s):  
Ruiqing Ni

Amyloid-beta (Aβ) plays an important role in the pathogenesis of Alzheimer’s disease. Aberrant Aβ accumulation induces neuroinflammation, cerebrovascular alterations, and synaptic deficits, leading to cognitive impairment. Animal models recapitulating the Aβ pathology, such as transgenic, knock-in mouse and rat models, have facilitated the understanding of disease mechanisms and the development of therapeutics targeting Aβ. There is a rapid advance in high-field MRI in small animals. Versatile high-field magnetic resonance imaging (MRI) sequences, such as diffusion tensor imaging, arterial spin labeling, resting-state functional MRI, anatomical MRI, and MR spectroscopy, as well as contrast agents, have been developed for preclinical imaging in animal models. These tools have enabled high-resolution in vivo structural, functional, and molecular readouts with a whole-brain field of view. MRI has been used to visualize non-invasively the Aβ deposits, synaptic deficits, regional brain atrophy, impairment in white matter integrity, functional connectivity, and cerebrovascular and glymphatic system in animal models of Alzheimer’s disease amyloidosis. Many of the readouts are translational toward clinical MRI applications in patients with Alzheimer’s disease. In this review, we summarize the recent advances in MRI for visualizing the pathophysiology in amyloidosis animal models. We discuss the outstanding challenges in brain imaging using MRI in small animals and propose future outlook in visualizing Aβ-related alterations in the brains of animal models.


2015 ◽  
Vol 12 (10) ◽  
pp. 1006-1011 ◽  
Author(s):  
Minori Yasue ◽  
Saiko Sugiura ◽  
Yasue Uchida ◽  
Hironao Otake ◽  
Masaaki Teranishi ◽  
...  

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.


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