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Personne :
Beheshti, Iman

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Beheshti

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Iman

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Université Laval. Département de radiologie et médecine nucléaire

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ncf11926829

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Voici les éléments 1 - 3 sur 3
  • PublicationAccès libre
    Patch-wise brain age longitudinal reliability (2020)
    (John Wiley & Sons, 2020-11-18) Potvin, Olivier; Beheshti, Iman; Duchesne, Simon
    We recently introduced a patch‐wise technique to estimate brain age from anatomical T1‐weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer acquired over a period of 17 years (aged 29–46 years) at multiple sites. We built a robust patch‐wise brain age estimation framework on the basis of 100 cognitively healthy individuals from the MindBoggle dataset (aged 19–61 years) using the Desikan‐Killiany‐Tourville atlas, then applied the model to the volunteer dataset. The results show a high prediction accuracy on the independent test set (R2 = .94, mean absolute error of 0.63 years) and no statistically significant difference between manufacturers, suggesting that the patch‐wise technique has high reliability and can be used for longitudinal multi‐centric studies.
  • PublicationAccès libre
    Braak neurofibrillary tangle staging prediction from in vivo MRI metrics
    (Elsevier, 2019-09-04) Potvin, Olivier; Beheshti, Iman; Dieumegarde, Louis; Duchesne, Simon; Dallaire-Théroux, Caroline; Saikali, Stephan
    INTRODUCTION: Alzheimer’s disease (AD) diagnosis requires postmortem visualization of amyloid and tau deposits. As brain atrophy can provide assessment of consequent neurodegeneration, our objective was to predict postmortem neurofibrillary tangles (NFT) from in vivo MRI measurements. METHODS: All participants with neuroimaging and neuropathological data from the Alzheimer’s Disease Neuroimaging Initiative, the National Alzheimer’s Coordinating Center and the Rush Memory and Aging Project were selected (n=186). 232 variables were extracted from last MRI before death using FreeSurfer. Nonparametric correlation analysis and multivariable support vector machine classification were performed to provide a predictive model of Braak NFT staging. RESULTS: We demonstrated that 59 of our MRI variables, mostly temporal lobe structures, were significantly associated with Braak NFT stages (p<.005). We obtained a 62.4% correct classification rate for discrimination between transentorhinal, limbic and isocortical groups. DISCUSSION: Structural neuroimaging may therefore be considered as a potential biomarker for early detection of AD-associated neurofibrillary degeneration.
  • PublicationRestreint
    A novel patch-based procedure for estimating brain age across adulthood
    (Academic Press, 2019-08-15) Potvin, Olivier; Gravel, Pierre; Beheshti, Iman; Dieumegarde, Louis; Duchesne, Simon
    Aging is associated with structural alterations in many regions of the brain. Monitoring these changes contributes to increasing our understanding of the brain's morphological alterations across its lifespan, and could allow the identification of departures from canonical trajectories. Here, we introduce a novel and unique patch-based grading procedure for estimating a synthetic estimate of cortical aging in cognitively intact individuals. The cortical age metric is computed based on image similarity between an unknown (test) cortical label and known (training) cortical labels using machine learning algorithms. The proposed method was trained on a dataset of 100 cognitively intact individuals aged 19–61 years, within the 31 bilateral cortical labels of the Desikan-KillianyTourville parcellation, then tested on an independent test set of 78 cognitively intact individuals spanning a similar age range. The proposed patch-based framework yielded a R2 ¼ 0.94, as well as a mean absolute error of 1.66 years, which compared favorably to the literature. These experimental results demonstrate that the proposed patch-based grading framework is a reliable and robust method to estimate brain age from image data, even with a limited training size.