scholarly journals Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images

2017 ◽  
Vol 33 ◽  
pp. 19-29 ◽  
Author(s):  
Ketil Oppedal ◽  
Kjersti Engan ◽  
Trygve Eftestøl ◽  
Mona Beyer ◽  
Dag Aarsland
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Lewy Body ◽  
Lewy Body Dementia ◽  
Magnetic Resonance Images ◽  
3D Texture ◽  
3D Texture Analysis ◽  
Normal Controls

scholarly journals ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer’s Disease by Means of Textures Analysis on Magnetic Resonance Images

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 47 ◽  
Author(s):  
Carlos López-Gómez ◽  
Rafael Ortiz-Ramón ◽  
Enrique Mollá-Olmos ◽  
David Moratal ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Software Tool ◽  
Magnetic Resonance Images ◽  
Gray Level ◽  
Predictive Capacity ◽  
Texture Parameters ◽  
New Biomarkers

The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.

Cognitive and structural magnetic resonance imaging features of Lewy body dementia and Alzheimer's disease

2012 ◽  
Vol 8 (3) ◽  
pp. 211-218 ◽  
Author(s):  
Miriam Jocelyn Rodriguez ◽  
Elizabeth Potter ◽  
Qian Shen ◽  
Warren Barker ◽  
Maria Greig-Custo ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Lewy Body ◽  
Lewy Body Dementia ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Structural Magnetic Resonance Imaging

scholarly journals P.052 Understanding the influence of APOE-ɛ4 on magnetic resonance imaging-based hippocampal phenotypes in Alzheimer’s disease and Lewy body dementia

2021 ◽  
Vol 48 (s3) ◽  
pp. S34-S34
Author(s):  
U Saeed ◽  
P Desmarais ◽  
M Masellis
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Lewy Body ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Lewy Body Dementia ◽  
Hippocampal Atrophy ◽  
Resonance Imaging

Background: The ɛ4-allele of apolipoprotein E (APOE-ɛ4) increases the risk not only for Alzheimer’s disease (AD), but also for Parkinson’s disease dementia and dementia with Lewy bodies (collectively, Lewy body dementia [LBD]). Hippocampal volume is an important neuroimaging biomarker for AD and LBD, although its association with APOE-ɛ4 is inconsistently reported. We investigated the association of APOE-ε4 with hippocampal atrophy quantified using magnetic resonance imaging in AD and LBD. Methods: Electronic databases (PubMed, Embase, PsycINFO, Scopus, Web of Science) were systematically searched for studies published up until December 31st, 2020. Results: Thirty-nine studies (25 cross-sectional, 14 longitudinal) were included. We observed that: (1) APOE-ε4 was associated with greater rate of hippocampal atrophy in AD and those who progressed from mild cognitive impairment to AD, (2) APOE-ε4 carriers showed greater involvement of cornu ammonis-1 hippocampal subfield versus non-carriers in AD, (3) APOE-ɛ4 may influence hippocampal atrophy in dementia with Lewy bodies, although longitudinal investigations are required, and (4) APOE-ε4 associated with earlier rather than very late expression of mediotemporal degeneration and memory-related neurocognitive impairment. Conclusions: The role of APOE-ɛ4 in modulating hippocampal phenotypes may be further clarified through more homogenous, well-powered, pathology-proven studies. Understanding the underlying mechanisms will facilitate development of prevention strategies targeting APOE-ɛ4.

3D texture analysis on MRI images of Alzheimer’s disease

2011 ◽  
Vol 6 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Jing Zhang ◽  
Chunshui Yu ◽  
Guilian Jiang ◽  
Weifang Liu ◽  
Longzheng Tong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Texture Analysis ◽  
3D Texture ◽  
3D Texture Analysis

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

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.

scholarly journals Functional connectivity of the nucleus basalis of Meynert in Lewy body dementia and Alzheimer’s disease

2021 ◽  
pp. 1-6
Author(s):  
Julia Schumacher ◽  
Alan J. Thomas ◽  
Luis R. Peraza ◽  
Michael Firbank ◽  
John T. O’Brien ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Lewy Body ◽  
Cholinergic System ◽  
Structural Changes ◽  
Lewy Body Dementia ◽  
Occipital Cortex ◽  
Nucleus Basalis ◽  
Nucleus Basalis Of Meynert

ABSTRACT Cholinergic deficits are a hallmark of Alzheimer’s disease (AD) and Lewy body dementia (LBD). The nucleus basalis of Meynert (NBM) provides the major source of cortical cholinergic input; studying its functional connectivity might, therefore, provide a tool for probing the cholinergic system and its degeneration in neurodegenerative diseases. Forty-six LBD patients, 29 AD patients, and 31 healthy age-matched controls underwent resting-state functional magnetic resonance imaging (fMRI). A seed-based analysis was applied with seeds in the left and right NBM to assess functional connectivity between the NBM and the rest of the brain. We found a shift from anticorrelation in controls to positive correlations in LBD between the right/left NBM and clusters in right/left occipital cortex. Our results indicate that there is an imbalance in functional connectivity between the NBM and primary visual areas in LBD, which provides new insights into alterations within a part of the corticopetal cholinergic system that go beyond structural changes.

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