Effects of aging on BOLD hemodynamic response: Healthy aging versus Alzheimer disease

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

doi:10.1109/iraniancee.2014.6999852

Cited by 2 publications

(3 citation statements)

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“…The noise contribution gets larger and more important through ageing [ 16 , 18 , 20 , 21 ]. For both the vision and motor regions, all-time series were first denoised by R-MDL for the young and non-demented and demented elderly adults as in [ 32 , 33 ], followed by the t -test for selecting activated voxels. The activated voxels have been selected as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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

Asemani

Morsheddost

Shalchy

2017

Healthcare Technology Letters

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Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors’ experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, denoising is a common preprocessing step that is applied before analysing the fMRI data. To more suppress the noise effects, minimum description length (MDL)-based wavelet method is here utilised as an extra denoising step before HRF extraction [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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

Asemani

Morsheddost

Shalchy

2017

Healthcare Technology Letters

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“…Although the shape of ݄ሺ‫ݐ‬ሻ in the healthy brain has been informed by the many works demonstrating the hemodynamic response function (Aguirre et al 1998;Shan et al 2014), different disease conditions have been known to alter the dCVR shape. For instance, aging (West et al, 2019), autism spectrum disorder (Yan et al, 2018) and Alzheimer's disease (Morsheddost et al, 2014) have all been known to alter the response shape. Thus, by evaluating all methods on a wide range of HRF timings and shapes, we can determine the suitability of these model-based methods for estimating an unknown ݄ሺ‫ݐ‬ሻ, one that may not conform to any of the four models.…”

Section: Ground-truth Dependence Of Modeling Methodsmentioning

confidence: 99%

Modeling the carbon-dioxide response function in fMRI under task and resting-state conditions

Shams

Prokopiou

Esmaelbeigi

et al. 2022

Preprint

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Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli. While the CVR amplitude has established clinical utility, the temporal characteristics of CVR have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various noise conditions. In this work, we present a comparison of several recently published model-based deconvolution approaches for estimating h(t), including maximum a posterior likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that allowed us to target a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the practical limitation that the true h(t) is unknown in methodological evaluations. Moreover, to best represent realistic noise found in fMRI scans, we extracted it from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can reasonably estimate dCVR even amidst high noise, and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method required the lowest computational time. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

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Effects of aging on BOLD hemodynamic response: Healthy aging versus Alzheimer disease

Cited by 2 publications

References 31 publications

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

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

Modeling the carbon-dioxide response function in fMRI under task and resting-state conditions

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