Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event‐related fMRI

Asemani, Davud; Morsheddost, Hassan; Shalchy, Mahsa Alizadeh

doi:10.1049/htl.2017.0005

Cited by 12 publications

(7 citation statements)

References 68 publications

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“…24 Also, a robust method applying minimum description length wavelet as an extra denoising step leads to better estimation of peaks in the hemodynamic response, which were found to vary significantly with age. 25 Statistically significant differences were found for the wavelet scale and the frequency parameters obtained by a series of wavelet analysis techniques between the normal and ADHD subjects. 26 In order to efficiently extract biomarkers from fMRI, both spatial and temporal features require to be captured and identified.…”

Section: Introductionmentioning

confidence: 93%

“…A method based on the characterization of scalograms that result from applying a continuous wavelet transform (CWT) to BOLD signals has been used to diagnose ADHD 24 . Also, a robust method applying minimum description length wavelet as an extra denoising step leads to better estimation of peaks in the hemodynamic response, which were found to vary significantly with age 25 . Statistically significant differences were found for the wavelet scale and the frequency parameters obtained by a series of wavelet analysis techniques between the normal and ADHD subjects 26…”

Section: Introductionmentioning

confidence: 99%

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A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

et al. 2021

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Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment. Methods: To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step. Results: Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%). Conclusion:The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

et al. 2021

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“…23 The hemodynamic response is known to be affected by cardiac and respiratory cycles, which are likely to be different between AD patients and healthy participants. 24,25 Therefore, it is possible that the differences found in previous papers can be attributed to such differences but not the underlying neuronal processes. These sources of noise should be correctly estimated in order to provide more accurate functional connectivity characterization.…”

Section: Introductionmentioning

confidence: 93%

Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer’s disease

Chang

Hsu

et al. 2020

J Cereb Blood Flow Metab

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The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD.

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“…They attained an average accuracy of 71.33% with all the proposed solutions. Davud Asemani et al developed a work on Alzheimer disease and its ageing effects on haemodynamic response function with fMRI [6]. They used MRI scans acquired using a 1.5 T Vision System with 128 scanned images.…”

Section: Related Alzheimer's and Parkinson's Diagnosis Workmentioning

confidence: 99%

Machine Learning Algorithms for the diagnosis of Alzheimer’s and Parkinson’s Disease

S¹

2020

IJATCSE

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Dementia is a general term used to indicate any disorder related to human memory. The various memory related problems severely affects the human brain and so the individual feels difficulty in doing their normal physical as well as mental activities. There are different types of dementia exists, but the commonly seen and fatal types of dementia are Alzheimer's disease (AD) and Parkinson's disease (PD). In this paper different efficient Machine Learning Techniques are selected analyzed their behaviors in the diagnosis of AD and PD using Positron Emission Tomography (PET). The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images. The total number of images is split into two different categories for training and testing in the ratio of 7:3. The different machine learning classifiers used are Bagged Ensemble, ID3, Naive Bayes and Multiclass Support Vector Machine. The classification of the AD and PD is carried out by comparing the test image with the trained samples in the database. On comparison of trained samples with the input image for the PET images, bagged ensemble learning classifier worked better than the other classification algorithms and yields an accuracy of 90.3%.

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Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event‐related fMRI

Cited by 12 publications

References 68 publications

A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

A robust DWT–CNN‐based CAD system for early diagnosis of autism using task‐based fMRI

Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer’s disease

Machine Learning Algorithms for the diagnosis of Alzheimer’s and Parkinson’s Disease

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