Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier

2020 ◽  
Vol 108 ◽  
pp. 101940
Author(s):  
Jinwang Feng ◽  
Shao-Wu Zhang ◽  
Luonan Chen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Wavelet Transformation ◽  
Structural Mri ◽  
Energy Feature ◽  
Mri Image

scholarly journals Detection of Alzheimer’s Disease-Related Cognitive Change With the Mobile Cognitive App Performance Platform

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 801-801
Author(s):  
Dawn Mechanic-Hamilton ◽  
Sean Lydon ◽  
Alexander Miller ◽  
Kimberly Halberstadter ◽  
Jacqueline Lane ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Memory Load ◽  
Memory Task ◽  
Structural Mri ◽  
Cognitive Change ◽  
Difficulty Level ◽  
Trial Duration ◽  
Parahippocampal Cortex ◽  
Relationship Of

Abstract This study investigates the psychometric properties of the mobile cognitive app performance platform (mCAPP), designed to detect memory changes associated with preclinical Alzheimer’s Disease (AD). The mCAPP memory task includes learning and matching hidden card pairs and incorporates increasing memory load, pattern separation features, and spatial memory. Participants included 30 older adults with normal cognition. They completed the mCAPP, paper and pencil neuropsychological tests and a subset completed a high-resolution structural MRI. The majority of participants found the difficulty level of the mCAPP game to be “just right”. Accuracy on the mCAPP correlated with performance on memory and executive measures, while speed of performance on the mCAPP correlated with performance on attention and executive function measures. Longer trial duration correlated with measures of the parahippocampal cortex. The relationship of mCAPP variables with molecular biomarkers, at-home and burst testing, and development of additional cognitive measures will also be discussed.

scholarly journals A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

NeuroImage ◽  
2020 ◽  
Vol 215 ◽  
pp. 116795 ◽  
Author(s):  
F.R. Farina ◽  
D.D. Emek-Savaş ◽  
L. Rueda-Delgado ◽  
R. Boyle ◽  
H. Kiiski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Structural Mri

Combining FDG-PET, structural MRI and probabilistic tractography in Alzheimer's disease

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S113
Author(s):  
SF Carter ◽  
GJM Parker ◽  
MA Lambon Ralph ◽  
K Herholz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fdg Pet ◽  
Structural Mri ◽  
Probabilistic Tractography

scholarly journals Imaging and Molecular Mechanisms of Alzheimer’s Disease: A Review

2018 ◽  
Vol 19 (12) ◽  
pp. 3702 ◽  
Author(s):  
Grazia Femminella ◽  
Tony Thayanandan ◽  
Valeria Calsolaro ◽  
Klara Komici ◽  
Giuseppe Rengo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multimodal Imaging ◽  
Molecular Mechanisms ◽  
Imaging Techniques ◽  
Structural Mri ◽  
Imaging Biomarkers ◽  
Significant Burden ◽  
Made In

Alzheimer’s disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer’s, as they provide an in vivo window to the pathological processes occurring in Alzheimer’s brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer’s disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer’s research and clinical practice.

scholarly journals A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Author(s):  
FR Farina ◽  
DD Emek-Savaş ◽  
L Rueda-Delgado ◽  
R Boyle ◽  
H Kiiski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Healthy Aging ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Structural Mri ◽  
Lower Accuracy

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

scholarly journals Progressive Volume Atrophy in Hippocampal Subfields and the Correlation with Cognition in Alzheimer’s Disease and Mild Cognitive Impairment

2021 ◽  
Author(s):  
Guixia Kang ◽  
Peiqi Luo ◽  
Xin Xu ◽  
Ying Han ◽  
Xuemei Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Reference Value ◽  
Structural Mri ◽  
Volume Changes ◽  
Brain Scans ◽  
Left Hippocampus ◽  
The Right

Abstract Objective: To assess the progression of volume changes in hippocampus and its subfields of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI), and to explore the association of the hippocampus and its subfields volumes with cognitive function.Methods: Five groups of participants including 35 normal controls (NC) persons, 30 MCI patients, 30 Mild AD patients, 30 Moderate AD patients and 8 Severe AD patients received structural MRI brain scans. Freesurfer6.0 was used for automatically segmentation of MRI, and the left and right hippocampus were respectively divided into 12 subfields. By statistical analysis, the volumes of hippocampus and its subfields were compared between the five groups, and the correlation of the volumes with Mini-mental State Examination (MMSE) score was analyzed.Result & Conclusion: In the disease, each hippocampal subfield shows an uneven atrophy trajectory; The volumes of the subiculum and presubiculum are significantly different between Mild AD and MCI, which can contribute to the early diagnosis of AD; Parasubiculum is the least sensitive subfield for volume atrophy of AD, while subiculum, presubiculum, CA1, molecular_layer_HP and fimbria show much more significant volume changes. Meanwhile the volumes of these five subfields are positively correlated with MMSE, which may help in stage division of AD; Compared with the right hippocampus, the volume atrophy on the left side is more significantly, and the volumes are more significantly correlated with MMSE, So the left hippocampus and its subfields may provide a higher reference value for the clinical evaluation of AD than the right side.

New Approaches for the Quantification and Targeting of Noradrenergic Dysfunction in Alzheimer’s Disease

2021 ◽  
Author(s):  
Michael David ◽  
Paresh A. Malhotra
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Locus Coeruleus ◽  
Neuropsychiatric Symptoms ◽  
Structural Mri ◽  
Brain Regions ◽  
Small Scale ◽  
Symptomatic Therapy ◽  
Pontine Nucleus ◽  
Individual Level

There is clear and early noradrenergic dysfunction in Alzheimer’s disease. This is likely secondary to pathological tau deposition in the locus coeruleus, the pontine nucleus that produces and releases noradrenaline, which occurs prior to involvement of cortical brain regions. Disruption of noradrenergic pathways affects cognition, especially attention, which impacts on memory and broader functioning in daily life. Additionally, it leads to the autonomic and neuropsychiatric symptoms that are frequently observed with disease progression.Despite the strong evidence of noradrenergic involvement in Alzheimer’s, there are no clear trial data supporting the clinical use of any noradrenergic treatments. Several approaches have been tried, including both proof-of-principle studies and randomised controlled trials, although most of the latter have been relatively small scale. Treatments have included drug therapies as well as stimulation modalities thought to modulate noradrenergic transmission. The lack of clear positive findings is likely secondary to limited capacity for gauging locus coeruleus integrity and noradrenergic dysfunction at an individual level. However, the recent development of several novel biomarkers holds potential and should allow quantification of dysfunction. This in turn may inform inclusion criteria and stratification for future trials. Imaging approaches in particular have improved greatly following the development of neuromelanin-sensitive sequences, enabling the use of structural MRI scans to estimate locus coeruleus integrity. Additionally, functional MRI and PET scanning have the potential to quantify network dysfunction. As well as neuroimaging, EEG and pupillometry techniques may prove useful in assessing noradrenergic tone and dynamic response to stimulation.Here we review the development of these biomarkers and discuss how they might augment clinical studies, particularly randomised trials, through stratification and identification of patients most likely to benefit from treatment. We outline the biomarkers with most potential, and how they hold promise as a means of transforming symptomatic therapy for people living with Alzheimer’s disease.

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