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Artikel ; Online: Predicting cognitive decline in a low-dimensional representation of brain morphology

Rémi Lamontagne-Caron / Patrick Desrosiers / Olivier Potvin / Nicolas Doyon / Simon Duchesne

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Band 18

Abstract: Abstract Identifying early signs of neurodegeneration due to Alzheimer’s disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging ...

Abstract	Abstract Identifying early signs of neurodegeneration due to Alzheimer’s disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18–100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ( $$n \ge 2$$ n ≥ 2 ), one year apart. The first embedding’s principal axis was shown to be positively associated ( $$r = 0.65$$ r = 0.65 ) with participants’ age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.
Schlagwörter	Medicine ; R ; Science ; Q
Thema/Rubrik (Code)	150
Sprache	Englisch
Erscheinungsdatum	2023-10-01T00:00:00Z
Verlag	Nature Portfolio
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Bias-adjustment in neuroimaging-based brain age frameworks

Iman Beheshti / Scott Nugent / Olivier Potvin / Simon Duchesne

NeuroImage: Clinical, Vol 24, Iss , Pp - (2019)

A robust scheme

2019

Abstract: The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using ... ...

Abstract	The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using chronological age as a covariate through the training set for downgrading the prediction bias in a Brain-age estimation framework. We applied proposed bias-adjustment scheme coupled by a machine learning-based brain age framework on a large set of metabolic brain features acquired from 675 cognitively unimpaired adults through fluorodeoxyglucose positron emission tomography data as the training set to build a robust Brain-age estimation framework. Then, we tested the reliability of proposed bias-adjustment scheme on 75 cognitively unimpaired adults, 561 mild cognitive impairment patients as well as 362 Alzheimer's disease patients as independent test sets. Using the proposed method, we gained a strong R2 of 0.81 between the chronological age and brain estimated age, as well as an excellent mean absolute error of 2.66 years on 75 cognitively unimpaired adults as an independent set; whereas an R2 of 0.24 and a mean absolute error of 4.71 years was achieved without bias-adjustment. The simulation results demonstrated that the proposed bias-adjustment scheme has a strong capability to diminish prediction error in brain age estimation frameworks for clinical settings. Keywords: Brain age, Estimation, Pet, Bias-adjustment, Brain metabolism
Schlagwörter	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
Thema/Rubrik (Code)	616
Sprache	Englisch
Erscheinungsdatum	2019-01-01T00:00:00Z
Verlag	Elsevier
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: FreeSurfer subcortical normative data

Olivier Potvin / Abderazzak Mouiha / Louis Dieumegarde / Simon Duchesne

Data in Brief, Vol 9, Iss C, Pp 732-

2016 Band 736

Abstract: This article contains a spreadsheet computing estimates of the expected subcortical regional volumes of an individual based on its characteristics and the scanner characteristics, in addition to supplementary results related to the article “Normative ... ...

Abstract	This article contains a spreadsheet computing estimates of the expected subcortical regional volumes of an individual based on its characteristics and the scanner characteristics, in addition to supplementary results related to the article “Normative data for subcortical regional volumes over the lifetime of the adult human brain” (O. Potvin, A. Mouiha, L. Dieumegarde, S. Duchesne, 2016) [1] on normative data for subcortical volumes. Data used to produce normative values was obtained by anatomical magnetic resonance imaging from 2790 healthy individuals aged 18–94 years using 23 samples provided by 21 independent research groups. The segmentation was conducted using FreeSurfer. The spreadsheet includes formulas in order to compute for a new individual, significance test for volume abnormality, effect size and estimated percentage of the normative population with a smaller volume while taking into account age, sex, estimated intracranial volume (eTIV), and scanner characteristics. Detailed R-squares of each predictor for all formula are also reported as well as the difference of subcortical volumes segmented by FreeSurfer on two different computer hardware setups.
Schlagwörter	Neuroimaging ; Age ; Sex ; Magnetic resonance ; Normality ; Normal aging ; Morphometry ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
Thema/Rubrik (Code)	310
Sprache	Englisch
Erscheinungsdatum	2016-12-01T00:00:00Z
Verlag	Elsevier
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Brain atrophy and patch-based grading in individuals from the CIMA-Q study

Christine Marcotte / Olivier Potvin / D. Louis Collins / Sylvie Rheault / Simon Duchesne

Scientific Reports, Vol 9, Iss 1, Pp 1-

a progressive continuum from subjective cognitive decline to AD

2019 Band 10

Abstract: Abstract It has been proposed that individuals developing Alzheimer’s disease (AD) first experience a phase expressing subjective complaints of cognitive decline (SCD) without objective cognitive impairment. Using magnetic resonance imaging (MRI), our ... ...

Abstract	Abstract It has been proposed that individuals developing Alzheimer’s disease (AD) first experience a phase expressing subjective complaints of cognitive decline (SCD) without objective cognitive impairment. Using magnetic resonance imaging (MRI), our objective was to verify whether SNIPE probability grading, a new MRI analysis technique, would distinguish between clinical dementia stage of AD: Cognitively healthy controls without complaint (CH), SCD, mild cognitive impairment, and AD. SNIPE score in the hippocampus and entorhinal cortex was applied to anatomical T1-weighted MRI of 143 participants from the Consortium pour l’identification précoce de la maladie Alzheimer - Québec (CIMA-Q) study and compared to standard atrophy measures (volumes and cortical thicknesses). Compared to standard atrophy measures, SNIPE score appeared more sensitive to differentiate clinical AD since differences between groups reached a higher level of significance and larger effect sizes. However, no significant difference was observed between SCD and CH groups. Combining both types of measures did not improve between-group differences. Further studies using a combination of biomarkers beyond anatomical MRI might be needed to identify individuals with SCD who are on the beginning of the clinical continuum of AD.
Schlagwörter	Medicine ; R ; Science ; Q
Thema/Rubrik (Code)	610
Sprache	Englisch
Erscheinungsdatum	2019-09-01T00:00:00Z
Verlag	Nature Publishing Group
Dokumenttyp	Artikel ; Online
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Artikel ; Online: Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer’s disease

Scott Nugent / Etienne Croteau / Olivier Potvin / Christian-Alexandre Castellano / Louis Dieumegarde / Stephen C. Cunnane / Simon Duchesne

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Band 8

Abstract: Abstract The primary method for measuring brain metabolism in humans is positron emission tomography (PET) imaging using the tracer 18F-fluorodeoxyglucose (FDG). Standardized uptake value ratios (SUVR) are commonly calculated from FDG-PET images to ... ...

Abstract	Abstract The primary method for measuring brain metabolism in humans is positron emission tomography (PET) imaging using the tracer 18F-fluorodeoxyglucose (FDG). Standardized uptake value ratios (SUVR) are commonly calculated from FDG-PET images to examine intra- and inter-subject effects. Various reference regions are used in the literature of FDG-PET studies of normal aging, making comparison between studies difficult. Our primary objective was to determine the optimal SUVR reference region in the context of healthy aging, using partial volume effect (PVE) and non-PVE corrected data. We calculated quantitative cerebral metabolic rates of glucose (CMRg) from PVE-corrected and non-corrected images from young and older adults. We also investigated regional atrophy using magnetic resonance (MR) images. FreeSurfer 6.0 atlases were used to explore possible reference regions of interest (ROI). Multiple regression was used to predict CMRg data, in each FreeSurfer ROI, with age and sex as predictors. Age had the least effect in predicting CMRg for PVE corrected data in the pons (r 2 = 2.83 × 10−3, p = 0.67). For non-PVE corrected data age also had the least effect in predicting CMRg in the pons (r 2 = 3.12 × 10−3, p = 0.67). We compared the effects of using the whole brain or the pons as a reference region in PVE corrected data in two regions susceptible to hypometabolism in Alzheimer’s disease, the posterior cingulate and precuneus. Using the whole brain as a reference region resulted in non-significant group differences in the posterior cingulate while there were significant differences between all three groups in the precuneus (all p < 0.004). When using the pons as a reference region there was significant differences between all groups for both the posterior cingulate and the precuneus (all p < 0.001). Therefore, the use of the pons as a reference region is more sensitive to hypometabism changes associated with Alzheimer’s disease than the whole brain.
Schlagwörter	Medicine ; R ; Science ; Q
Sprache	Englisch
Erscheinungsdatum	2020-06-01T00:00:00Z
Verlag	Nature Portfolio
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Daniel Gourdeau / Olivier Potvin / Patrick Archambault / Carl Chartrand-Lefebvre / Louis Dieumegarde / Reza Forghani / Christian Gagné / Alexandre Hains / David Hornstein / Huy Le / Simon Lemieux / Marie-Hélène Lévesque / Diego Martin / Lorne Rosenbloom / An Tang / Fabrizio Vecchio / Issac Yang / Nathalie Duchesne / Simon Duchesne

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Band 14

Abstract: Abstract Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity ... ...

Abstract	Abstract Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: ‘Worse’, ‘Stable’, or ‘Improved’ on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between “Worse” and “Improved” outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic (‘Consolidation’, ‘Lung Lesion’, ‘Pleural effusion’ and ‘Pneumonia’; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between ‘Worse’ and ‘Improved’ cases with a 0.81 (0.74–0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67–0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR ...
Schlagwörter	Medicine ; R ; Science ; Q
Thema/Rubrik (Code)	006
Sprache	Englisch
Erscheinungsdatum	2022-04-01T00:00:00Z
Verlag	Nature Portfolio
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol

AmanPreet Badhwar / Yannik Collin-Verreault / Desiree Lussier / Hanad Sharmarke / Pierre Orban / Sebastian Urchs / Isabelle Chouinard / Jacob Vogel / Olivier Potvin / Simon Duchesne / Pierre Bellec

Data in Brief, Vol 31, Iss , Pp 105699- (2020)

2020

Abstract: The impact of multisite acquisition on resting-state functional MRI (rsfMRI) connectivity has recently gained attention. We provide consistency values (Pearson's correlation) between rsfMRI connectivity maps of an adult volunteer (Csub) scanned 25 times ... ...

Abstract	The impact of multisite acquisition on resting-state functional MRI (rsfMRI) connectivity has recently gained attention. We provide consistency values (Pearson's correlation) between rsfMRI connectivity maps of an adult volunteer (Csub) scanned 25 times over 3.5 years at 13 sites using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). This dataset was generated as part of the following article: Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors [1]. Acquired on three 3T scanner vendors (GE, Siemens and Philips), the Csub dataset is part of an ongoing effort to monitor the quality and comparability of MRI data collected across the Canadian Consortium on Neurodegeneration in Aging (CCNA) imaging network. The participant was scanned 25 times in the above-mentioned article: multiple times at six sites over a period of 2.5 years, and once at the remaining seven sites. Since then the participant was scanned an additional 45 times, allowing us to extend the dataset to 70 rsfMRI scans over a period of >4 years.In addition, we provide intra- and inter-subject consistency values of rsfMRI connectivity maps derived from 26 adult participants belonging to the publicly released Hangzhou Normal University dataset (HNU1). All HNU1 participants underwent 10 rsfMRI scans over one month on a single 3T scanner (GE).Connectivity maps of seven canonical networks were generated for each scan in the two datasets (Csub and HNU1). All consistency values, along with the scripts used to preprocess the rsfMRI data and generate connectivity maps and pairwise consistency values, have been made available on two public repositories, Github and Zenodo. We have also made available four Jupyter notebooks that use the provided consistency values to (a) generate interactive graphical summaries – 1 notebook, (b) perform statistical analyses - 2 notebooks, and (c) perform data-driven cluster analysis for the recovery of subject identity (i.e. rsfMRI ...
Schlagwörter	Resting-state fMRI ; Multisite ; Consistency values ; Fingerprinting ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
Sprache	Englisch
Erscheinungsdatum	2020-08-01T00:00:00Z
Verlag	Elsevier
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Band 10

Abstract: Abstract The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk ...

Abstract	Abstract The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.
Schlagwörter	Medicine ; R ; Science ; Q
Thema/Rubrik (Code)	690
Sprache	Englisch
Erscheinungsdatum	2022-04-01T00:00:00Z
Verlag	Nature Portfolio
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Structural and functional multi-platform MRI series of a single human volunteer over more than fifteen years

Simon Duchesne / Louis Dieumegarde / Isabelle Chouinard / Farnaz Farokhian / Amanpreet Badhwar / Pierre Bellec / Pascal Tétreault / Maxime Descoteaux / Arnaud Boré / Jean-Christophe Houde / Christian Beaulieu / Olivier Potvin

Scientific Data, Vol 6, Iss 1, Pp 1-

2019 Band 9

Abstract: Measurement(s)brainTechnology Type(s)magnetic resonance imagingSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare ... ...

Abstract	Measurement(s)brainTechnology Type(s)magnetic resonance imagingSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9925037
Schlagwörter	Science ; Q
Sprache	Englisch
Erscheinungsdatum	2019-10-01T00:00:00Z
Verlag	Nature Publishing Group
Dokumenttyp	Artikel ; Online
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Artikel ; Online: The Canadian Dementia Imaging Protocol

Olivier Potvin / Isabelle Chouinard / Louis Dieumegarde / Robert Bartha / Pierre Bellec / D. Louis Collins / Maxime Descoteaux / Rick Hoge / Joel Ramirez / Christopher J.M. Scott / Eric E. Smith / Stephen C. Strother / Sandra E. Black / Simon Duchesne

NeuroImage: Clinical, Vol 24, Iss , Pp - (2019)

Harmonization validity for morphometry measurements

2019

Abstract: The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of ... ...

Abstract	The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of CDIP parameters compliance on total brain volume variance using 86 scans of the same individual acquired on various scanners. Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. For images acquired from Philips scanners, lower variance in brain volumes were observed when the stated CDIP resolution was set. For images acquired from GE scanners, lower variance in brain volumes were noticed when TE/TR values were within 5% of the CDIP protocol, compared to values farther from that criteria. Together, these results suggest that a harmonized protocol like the CDIP may help to reduce neuromorphometric measurement variability in multi-centric studies. Keywords: Neuroimaging, Magnetic resonance imaging, Standardization, Morphometry, Multi-centric studies
Schlagwörter	Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
Sprache	Englisch
Erscheinungsdatum	2019-01-01T00:00:00Z
Verlag	Elsevier
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Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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