45 results on '"Guizard, Nicolas"'
Search Results
2. CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
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Jesson, Andrew, Guizard, Nicolas, Ghalehjegh, Sina Hamidi, Goblot, Damien, Soudan, Florian, and Chapados, Nicolas
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem., Comment: 20th International Conference on Medical Image Computing and Computer Assisted Intervention 2017
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- 2018
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3. Clinical Phenotypes of Alzheimer's Disease: Atrophy Patterns and their Pathological Correlates.
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Reijner, Niels, Frigerio, Irene, Bouwman, Maud M.A., Boon, Baayla D.C., Guizard, Nicolas, Jubault, Thomas, Hoozemans, Jeroen J.M., Rozemuller, Annemieke J.M., Bouwman, Femke H., Barkhof, Frederik, Gordon, Elizabeth, van de Berg, Wilma D.J., and Jonkman, Laura E.
- Abstract
Background: Recent studies highlight distinct patterns of cortical atrophy between amnestic (typical) and non‐amnestic (atypical, with subtypes: behavioural, dysexecutive, logopenic and visuospatial) clinical phenotypes of Alzheimer's disease (AD). The current study aimed to assess regional MRI patterns of cortical atrophy across AD phenotypes, and their association with amyloid‐beta (Aβ), phosphorylated tau (pTau), axonal degeneration (NfL) and microvascular deterioration (COLIV). Method: Postmortem In‐situ 3DT1 3T‐MRI data was collected for 33 AD (17 typical, 16 atypical) and 16 control brain donors. Images were segmented and AAL3 atlas regional volumes were obtained using QyScore®. At subsequent autopsy, eight brain regions were selected, immunostained for Aβ (4G8), pTau (AT8), Neurofilament‐light (NFL), and Collagen‐IV (COLIV), and quantified using qupath. Group comparisons and volume‐pathology associations were analyzed using linear models and partial correlations with covariates age, sex, postmortem delay, and intracranial volume. Results: Compared to controls, AD phenotype groups showed overall lower cortical volume, while only minor volume differences were observed between AD phenotype groups, observed primarily in limbic regions (Fig. 1). Across pathological markers, AD phenotype groups showed consistently higher immunoreactivity than controls, while atypical AD showed consistently higher immunoreactivity than typical AD (Fig. 2). Moreover, different patterns of pathology could be observed between atypical subtypes (e.g. distinctly higher pTau load in the occipital gyrus of the visuospatial subtype). In typical AD, global volume loss was associated with lower Aβ and higher pTau, NFL and COLIV immunoreactivity, while in atypical AD, global volume loss was primarily associated with higher NFL immunoreactivity (Fig. 3a). Regionally, AD phenotype differences in atrophy‐pathology association were most pronounced in the (para) hippocampal regions. This distinction was mainly characterized by negative associations for NFL and COLIV, which was only observed in typical AD (Fig. 3b‐c). Conclusion: Atrophy patterns between AD phenotypes showed only minor differences, potentially attributable to the on average later disease stage of the study cohort. The higher immunoreactivity for pathological markers found in atypical AD might suggest a more severe disease burden. (Para) hippocampal volume decline was associated with axonal and microvascular deterioration in typical AD, but not in the higher pathologically burdened atypical AD group, suggesting a differential susceptibility between AD phenotypes. [ABSTRACT FROM AUTHOR]
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- 2024
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4. Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects
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Tran, Philippe, Thoprakarn, Urielle, Gourieux, Emmanuelle, dos Santos, Clarisse Longo, Cavedo, Enrica, Guizard, Nicolas, Cotton, François, Krolak-Salmon, Pierre, Delmaire, Christine, Heidelberg, Damien, Pyatigorskaya, Nadya, Ströer, Sébastian, Dormont, Didier, Martini, Jean-Baptiste, and Chupin, Marie
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- 2022
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5. HeMIS: Hetero-Modal Image Segmentation
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Havaei, Mohammad, Guizard, Nicolas, Chapados, Nicolas, and Bengio, Yoshua
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Computer Science - Computer Vision and Pattern Recognition - Abstract
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches., Comment: Accepted as an oral presentation at MICCAI 2016
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- 2016
6. Deep learning trends for focal brain pathology segmentation in MRI
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Havaei, Mohammad, Guizard, Nicolas, Larochelle, Hugo, and Jodoin, Pierre-Marc
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease development as well as other applications such as tractography. Over the years, attempts have been made to automate this process for both clinical and research reasons. In this regard, machine learning methods have long been a focus of attention. Over the past two years, the medical imaging field has seen a rise in the use of a particular branch of machine learning commonly known as deep learning. In the non-medical computer vision world, deep learning based methods have obtained state-of-the-art results on many datasets. Recent studies in computer aided diagnostics have shown deep learning methods (and especially convolutional neural networks - CNN) to yield promising results. In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation. In particular, we discuss their characteristic peculiarities and their specific configuration and adjustments that are best suited to segment medical images. We also underline the intrinsic differences deep learning methods have with other machine learning methods., Comment: Published in Machine Learning for Health Informatics
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- 2016
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7. Clinical Phenotypes of Alzheimer’s Disease: Pathological Correlates of Regional Cortical Volume
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Reijner, Niels, primary, Frigerio, Irene, additional, Bouwman, Maud M.A., additional, Guizard, Nicolas, additional, Jubault, Thomas, additional, Lin, Chen‐Pei, additional, Hoozemans, Jeroen, additional, Rozemuller, Annemieke J.M., additional, Bouwman, Femke H., additional, Barkhof, Frederik, additional, Gordon, Elizabeth, additional, Berg, Wilma D.J., additional, and Jonkman, Laura E., additional
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- 2023
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8. QyPredict prognostic model enriches selection for faster decliners in mild cognitive impairment
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Gordon, Elizabeth, primary, Samper‐González, Jorge, additional, Villa, Luca, additional, Jubault, Thomas, additional, and Guizard, Nicolas, additional
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- 2023
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9. An automated pipeline for Centiloid quantification of amyloid‐β using multiple 11C‐PiB‐PET and 18F‐PET tracers
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Gordon, Elizabeth, primary, Borrot, Mathilde, additional, Gueddou, Ayoub, additional, Jubault, Thomas, additional, and Guizard, Nicolas, additional
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- 2023
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10. Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation 1
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Havaei, Mohammad, primary, Guizard, Nicolas, additional, Chapados, Nicolas, additional, and Bengio, Yoshua, additional
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- 2018
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11. Validation of QyScore's® fully automated quantitative image segmentation tools against expert manual gold‐standards.
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Gordon, Elizabeth, Borrot, Mathilde, Villa, Luca, Jubault, Thomas, and Guizard, Nicolas
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Background: Quantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard. Method: The validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression. Result: Mean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®'s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2). Conclusion: QyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge
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Cash, David M., Frost, Chris, Iheme, Leonardo O., Ünay, Devrim, Kandemir, Melek, Fripp, Jurgen, Salvado, Olivier, Bourgeat, Pierrick, Reuter, Martin, Fischl, Bruce, Lorenzi, Marco, Frisoni, Giovanni B., Pennec, Xavier, Pierson, Ronald K., Gunter, Jeffrey L., Senjem, Matthew L., Jack, Clifford R., Jr., Guizard, Nicolas, Fonov, Vladimir S., Collins, D. Louis, Modat, Marc, Cardoso, M. Jorge, Leung, Kelvin K., Wang, Hongzhi, Das, Sandhitsu R., Yushkevich, Paul A., Malone, Ian B., Fox, Nick C., Schott, Jonathan M., and Ourselin, Sebastien
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- 2015
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13. Rotation-invariant multi-contrast non-local means for MS lesion segmentation
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Guizard, Nicolas, Coupé, Pierrick, Fonov, Vladimir S., Manjón, Jose V., Arnold, Douglas L., and Collins, D. Louis
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- 2015
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14. Spatio-temporal Regularization for Longitudinal Registration to an Unbiased 3D Individual Template
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Guizard, Nicolas, Fonov, Vladimir S., García-Lorenzo, Daniel, Aubert-Broche, Bérengère, Eskildsen, Simon F., Collins, D. Louis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Durrleman, Stanley, editor, Fletcher, Tom, editor, Gerig, Guido, editor, and Niethammer, Marc, editor
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- 2012
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15. A New Framework for Analyzing Structural Volume Changes of Longitudinal Brain MRI Data
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Aubert-Broche, Bérengère, Fonov, Vladimir S., García-Lorenzo, Daniel, Mouiha, Abderazzak, Guizard, Nicolas, Coupé, Pierrick, Eskildsen, Simon F., Collins, D. Louis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Durrleman, Stanley, editor, Fletcher, Tom, editor, Gerig, Guido, editor, and Niethammer, Marc, editor
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- 2012
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16. Robust 3D Reconstruction and Mean-Shift Clustering of Motoneurons from Serial Histological Images
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Guizard, Nicolas, Coupe, Pierrick, Stifani, Nicolas, Stifani, Stefano, Collins, D. Louis, Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Liao, Hongen, editor, Edwards, P. J. 'Eddie", editor, Pan, Xiaochuan, editor, Fan, Yong, editor, and Yang, Guang-Zhong, editor
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- 2010
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17. Jacobian integration method increases the statistical power to measure gray matter atrophy in multiple sclerosis
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Nakamura, Kunio, Guizard, Nicolas, Fonov, Vladimir S., Narayanan, Sridar, Collins, D. Louis, and Arnold, Douglas L.
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- 2014
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18. CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
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Jesson, Andrew, primary, Guizard, Nicolas, additional, Ghalehjegh, Sina Hamidi, additional, Goblot, Damien, additional, Soudan, Florian, additional, and Chapados, Nicolas, additional
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- 2017
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19. BEaST: Brain extraction based on nonlocal segmentation technique
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Eskildsen, Simon F., Coupé, Pierrick, Fonov, Vladimir, Manjón, José V., Leung, Kelvin K., Guizard, Nicolas, Wassef, Shafik N., Østergaard, Lasse Riis, and Collins, D. Louis
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- 2012
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20. An automated pipeline for Centiloid quantification of amyloid‐β using multiple 11C‐PiB‐PET and 18F‐PET tracers.
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Gordon, Elizabeth, Borrot, Mathilde, Gueddou, Ayoub, Jubault, Thomas, and Guizard, Nicolas
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Background: Quantitative measures of amyloid‐β (Aβ) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer's disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aβ‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds. Method: QyScore's® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) > 0.7 were required to consider the Centiloid calibration valid. Result: QyScore's® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3). Equations for converting to Centiloid were respectively o Centiloid = 177.79 FBP_SUVr ‐ 183.56 o Centiloid = 153.08 FBB_SUVr – 152.93 o Centiloid = 122.39 FTM_SUVr – 120.97 o Centiloid = 90.20 NAV_SUVr – 91.61 Conclusion: We demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials. [ABSTRACT FROM AUTHOR]
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- 2023
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21. HeMIS: Hetero-Modal Image Segmentation
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Havaei, Mohammad, primary, Guizard, Nicolas, additional, Chapados, Nicolas, additional, and Bengio, Yoshua, additional
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- 2016
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22. Deep Learning Trends for Focal Brain Pathology Segmentation in MRI
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Havaei, Mohammad, primary, Guizard, Nicolas, additional, Larochelle, Hugo, additional, and Jodoin, Pierre-Marc, additional
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- 2016
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23. Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease
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Haegelen, Claire, Coupé, Pierrick, Fonov, Vladimir, Guizard, Nicolas, Jannin, Pierre, Morandi, Xavier, and Collins, D. Louis
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- 2013
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24. Regional Cerebellar Volumes Predict Functional Outcome in Children with Cerebellar Malformations
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Bolduc, Marie-Eve, du Plessis, Adre J., Sullivan, Nancy, Guizard, Nicolas, Zhang, Xun, Robertson, Richard L., and Limperopoulos, Catherine
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- 2012
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25. Normative fetal brain growth by quantitative in vivo magnetic resonance imaging
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Clouchoux, Cedric, Guizard, Nicolas, Evans, Alan Charles, du Plessis, Adre Jacques, and Limperopoulos, Catherine
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- 2012
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26. Investigation of Morphometric Variability of Subthalamic Nucleus, Red Nucleus, and Substantia Nigra in Advanced Parkinsonʼs Disease Patients Using Automatic Segmentation and PCA-Based Analysis
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Xiao, Yiming, Jannin, Pierre, DʼAlbis, Tiziano, Guizard, Nicolas, Haegelen, Claire, Lalys, Florent, Vérin, Marc, and Collins, Louis D.
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- 2014
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27. Injury to the Premature Cerebellum: Outcome is Related to Remote Cortical Development
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Chilingaryan, Gevorg, Limperopoulos, Catherine, Sullivan, Nancy, Guizard, Nicolas, Robertson, Richard L., and du Plessis, Adré J.
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- 2014
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28. Spatio-temporal Regularization for Longitudinal Registration to an Unbiased 3D Individual Template
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Guizard, Nicolas, primary, Fonov, Vladimir S., additional, García-Lorenzo, Daniel, additional, Aubert-Broche, Bérengère, additional, Eskildsen, Simon F., additional, and Collins, D. Louis, additional
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- 2012
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29. A New Framework for Analyzing Structural Volume Changes of Longitudinal Brain MRI Data
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Aubert-Broche, Bérengère, primary, Fonov, Vladimir S., additional, García-Lorenzo, Daniel, additional, Mouiha, Abderazzak, additional, Guizard, Nicolas, additional, Coupé, Pierrick, additional, Eskildsen, Simon F., additional, and Collins, D. Louis, additional
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- 2012
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30. Cerebellar malformations alter regional cerebral development
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BOLDUC, MARIE-EVE, DU PLESSIS, ADRE J, EVANS, ALAN, GUIZARD, NICOLAS, ZHANG, XUN, ROBERTSON, RICHARD L, and LIMPEROPOULOS, CATHERINE
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- 2011
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31. 256 – Artificial Intelligence for Real-Time Multiple Polyp Detection with Identification, Tracking, and Optical Biopsy During Colonoscopy
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Guizard, Nicolas, primary, Ghalehjegh, Sina Hamidi, additional, Henkel, Milagros, additional, Ding, Liqiang, additional, Shahidi, Neal C., additional, Jonathan, Garcia R., additional, Lahr, Rachel, additional, Chandelier, Florent, additional, Rex, Doug, additional, and Byrne, Michael F., additional
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- 2019
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32. Mo1679 REAL-TIME ARTIFICIAL INTELLIGENCE “FULL COLONOSCOPY WORKFLOW” FOR AUTOMATIC DETECTION FOLLOWED BY OPTICAL BIOPSY OF COLORECTAL POLYPS
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Byrne, Michael F., primary, Soudan, Florian, additional, Henkel, Milagros, additional, Oertel, Clemens, additional, Chapados, Nicolas, additional, Echagüe, Francisco J., additional, Ghalehjegh, Sina Hamidi, additional, Guizard, Nicolas, additional, Giguère, Sébastien, additional, MacPhail, Margaret E., additional, Sullivan, Andrew, additional, Chandelier, Florent, additional, and Rex, Douglas K., additional
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- 2018
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33. Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training
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Valk, Sofie L., primary, Bernhardt, Boris C., additional, Trautwein, Fynn-Mathis, additional, Böckler-Raettig, Anne, additional, Kanske, Philipp, additional, Guizard, Nicolas, additional, Collins, D. Louis, additional, and Singer, Tania, additional
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- 2017
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34. BEaST:brain extraction using multiresolution nonlocal segmentation
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Eskildsen, Simon Fristed, Coupé, Pierrick, Leung, Kelvin K., Fonov, Vladimir, Guizard, Nicolas, Wassef, Shafik N., Østergaard, Lasse Riis, and Collins, D. Louis
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- 2011
35. Non-Local Means Inpainting of MS Lesions in Longitudinal Image Processing
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Guizard, Nicolas, primary, Nakamura, Kunio, additional, Coupé, Pierrick, additional, Fonov, Vladimir S., additional, Arnold, Douglas L., additional, and Collins, D. Louis, additional
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- 2015
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36. Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template
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Guizard, Nicolas, primary, Fonov, Vladimir S., additional, García-Lorenzo, Daniel, additional, Nakamura, Kunio, additional, Aubert-Broche, Bérengère, additional, and Collins, D. Louis, additional
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- 2015
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37. BEaST: Brain extraction based on nonlocal segmentation technique
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Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada, Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació, Ministerio de Ciencia e Innovación, National Institutes of Health, EEUU, Canadian Institutes of Health Research, Northern California Institute for Research and Education, National Institute of Biomedical Imaging and Bioengineering, EEUU, National Institute on Aging, EEUU, Dana Foundation, DoD Alzheimer's Disease Neuroimaging Initiative, Alzheimer's Research UK, Eskildsen, Simon F., Coupé, Pierrick, Fonov, Vladimir, Manjón Herrera, José Vicente, Leung, Kelvin K., Guizard, Nicolas, Wassef, Shafik N., Østergaard, Lasse Riis, Collins, D. Louis, Alzheimer's Dis Neuroimaging, Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada, Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació, Ministerio de Ciencia e Innovación, National Institutes of Health, EEUU, Canadian Institutes of Health Research, Northern California Institute for Research and Education, National Institute of Biomedical Imaging and Bioengineering, EEUU, National Institute on Aging, EEUU, Dana Foundation, DoD Alzheimer's Disease Neuroimaging Initiative, Alzheimer's Research UK, Eskildsen, Simon F., Coupé, Pierrick, Fonov, Vladimir, Manjón Herrera, José Vicente, Leung, Kelvin K., Guizard, Nicolas, Wassef, Shafik N., Østergaard, Lasse Riis, Collins, D. Louis, and Alzheimer's Dis Neuroimaging
- Abstract
Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI)., Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 ± 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 ± 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.
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- 2012
38. Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease
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Haegelen, Claire, primary, Coupé, Pierrick, additional, Fonov, Vladimir, additional, Guizard, Nicolas, additional, Jannin, Pierre, additional, Morandi, Xavier, additional, and Collins, D. Louis, additional
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- 2012
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39. Regional Cerebellar Volumes Predict Functional Outcome in Children with Cerebellar Malformations
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Bolduc, Marie-Eve, primary, du Plessis, Adre J., additional, Sullivan, Nancy, additional, Guizard, Nicolas, additional, Zhang, Xun, additional, Robertson, Richard L., additional, and Limperopoulos, Catherine, additional
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- 2011
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40. Cerebellar Injury in the Premature Infant Is Associated With Impaired Growth of Specific Cerebral Regions
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Limperopoulos, Catherine, primary, Chilingaryan, Gevorg, additional, Guizard, Nicolas, additional, Robertson, Richard L, additional, and Du Plessis, Adré J, additional
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- 2010
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41. Brain Volume and Metabolism in Fetuses With Congenital Heart Disease
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Limperopoulos, Catherine, primary, Tworetzky, Wayne, additional, McElhinney, Doff B., additional, Newburger, Jane W., additional, Brown, David W., additional, Robertson, Richard L., additional, Guizard, Nicolas, additional, McGrath, Ellen, additional, Geva, Judith, additional, Annese, David, additional, Dunbar-Masterson, Carolyn, additional, Trainor, Bethany, additional, Laussen, Peter C., additional, and du Plessis, Adré J., additional
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- 2010
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42. Injury to the Premature Cerebellum: Outcome is Related to Remote Cortical Development.
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Limperopoulos, Catherine, Chilingaryan, Gevorg, Sullivan, Nancy, Guizard, Nicolas, Robertson, Richard L., and du Plessis, Adré J.
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- 2014
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43. Structural plasticity of the social brain: Differential change after socio-affective and cognitive mental training.
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Valk, Sofie L., Bernhardt, Boris C., Trautwein, Fynn-Mathis, Bockler, Anne, Kanske, Philipp, Guizard, Nicolas, Collins, D. Louis, and Singer, Tania
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MENTAL training , *SOCIAL skills , *BRAIN , *MAGNETIC resonance imaging , *MINDFULNESS - Abstract
Although neuroscientific research has revealed experience-dependent brain changes across the life span in sensory, motor, and cognitive domains, plasticity relating to social capacities remains largely unknown. To investigate whether the targeted mental training of different cognitive and social skills can induce specific changes in brain morphology, we collected longitudinal magnetic resonance imaging (MRI) data throughout a 9-month mental training intervention from a large sample of adults between 20 and 55 years of age. By means of various daily mental exercises and weekly instructed group sessions, training protocols specifically addressed three functional domains: (i) mindfulness-based attention and interoception, (ii) socio-affective skills (compassion, dealing with difficult emotions, and prosocial motivation), and (iii) socio-cognitive skills (cognitive perspective-taking on self and others and metacognition). MRI-based cortical thickness analyses, contrasting the different training modules against each other, indicated spatially diverging changes in cortical morphology. Training of present-moment focused attention mostly led to increases in cortical thickness in prefrontal regions, socio-affective training induced plasticity in frontoinsular regions, and socio-cognitive training included change in inferior frontal and lateral temporal cortices. Module-specific structural brain changes correlated with training-induced behavioral improvements in the same individuals in domain-specific measures of attention, compassion, and cognitive perspective-taking, respectively, and overlapped with task-relevant functional networks. Our longitudinal findings indicate structural plasticity in well-known socio-affective and socio-cognitive brain networks in healthy adults based on targeted short daily mental practices. These findings could promote the development of evidence-based mental training interventions in clinical, educational, and corporate settings aimed at cultivating social intelligence, prosocial motivation, and cooperation. [ABSTRACT FROM AUTHOR]
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- 2017
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44. Robust individual template pipeline for longitudinal MR images
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Nicolas Guizard, Vladimir Fonov, Bérengère Aubert-Broche, Daniel García-Lorenzo, Pierrick Coupe, Simon Eskildsen, Louis Collins, D., Guizard, Nicolas, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Centre de Neuro-Imagerie de Recherche (CENIR), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Aarhus University [Aarhus], Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris] (CENIR), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
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[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging ,longitudinal ,[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ,segmentation ,pipeline ,template ,mri - Abstract
International audience; Longitudinal measures of brain volume are powerful tools to assess the anatomical changes underlying on-going neurodegenerative processes. In different neurological disorders, such as in multiple sclerosis, Alzheimer's disease and Parkinson's disease, the neurodegenerative aspect may result in subtle anatomical brain changes before the appearance of clinical symptoms. Large longitudinal brain imaging datasets are now accessible to investigate such structural changes over time and to evaluate their use as biomarkers of prodromal disease. However, manual segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each visit is analysed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. MR scanner noise and physiological effects can also introduce additional variability. Therefore, we developed a specific pipeline for longitudinal brain image analysis. To avoid any bias, an individual subject template is created and used as a reference within the pipeline. Then, the pair-wise deformation fields of each visit to the individual template are used to estimate the variation between individual time-points.
45. Blarcamesine for the treatment of Early Alzheimer's Disease: Results from the ANAVEX2-73-AD-004 Phase IIB/III trial.
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Macfarlane S, Grimmer T, Teo K, O'Brien TJ, Woodward M, Grunfeld J, Mander A, Brodtmann A, Brew BJ, Morris P, Short C, Kurrle S, Lai R, Bharadwaj S, Drysdale P, Sturm J, Lewis SJG, Barton D, Kalafatis C, Sharif S, Perry R, Mannering N, MacSweeney JE, Pearson S, Evans C, Krishna V, Thompson A, Munisamy M, Bhatt N, Asher A, Connell S, Lynch J, Rutgers SM, Dautzenberg PL, Prins N, Oschmann P, Frölich L, Tacik P, Peters O, Wiltfang J, Henri-Bhargava A, Smith E, Pasternak S, Frank A, Chertkow H, Ingram J, Hsiung GR, Brittain R, Tartaglia C, Cohen S, Villa LM, Gordon E, Jubault T, Guizard N, Tucker A, Kaufmann WE, Jin K, Chezem WR, Missling CU, and Sabbagh MN
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- Humans, Double-Blind Method, Male, Female, Aged, Receptors, sigma, Sigma-1 Receptor, Amyloid beta-Peptides, Middle Aged, Treatment Outcome, Aged, 80 and over, Alzheimer Disease drug therapy
- Abstract
Background: There are no approved oral disease-modifying treatments for Alzheimer's disease (AD)., Objectives: The objective of this study was to assess efficacy and safety of blarcamesine (ANAVEX®2-73), an orally available small-molecule activator of the sigma-1 receptor (SIGMAR1) in early AD through restoration of cellular homeostasis including autophagy enhancement., Design: ANAVEX2-73-AD-004 was a randomized, double-blind, placebo-controlled, 48-week Phase IIb/III trial., Setting: Multicenter - 52 medical research centers/hospitals in 5 countries., Intervention: 508 participants with early AD (Stage 3) were randomized to receive either blarcamesine (n = 338) in medium dose group 30 mg or in high dose group 50 mg or placebo (n = 170) oral capsules once daily for 48 weeks. Participants in these groups were offered to enroll into the open-label-extension study ATTENTION-AD, which completed June 2024, ClinicalTrials.gov Identifier NCT04314934., Measurements: The co-primary cognitive and functional outcomes were assessed as change in ADAS-Cog13 and ADCS-ADL from baseline to 48 weeks. The outcomes include the secondary outcome CDR-SB and biomarkers from the A/T/N spectrum, plasma Aβ42/40-ratio and global brain volume changes measured by MRI. All clinical endpoints were analyzed using mixed model for repeated measures (MMRM), plasma biomarker measurements were analyzed by Welch's t-test, and volumetric MRI scans were analyzed by general linear model., Results: Among 462 randomized participants in the intent-to-treat population (mean age, 73.7 years; 225 [48.7%] women), 338 (73.2%) completed the trial. The co-primary outcome was met under the multiplicity control rule, since the differences in the least-squares mean (LSM) change from baseline to 48 weeks between the prespecified blarcamesine and placebo groups for ADAS-Cog13 was significant at a level of P < 0.025 and for CDR-SB was significant at a level of P < 0.025, while ADCS-ADL did not reach significance at Week 48 (ADAS-Cog13 difference of -2.027 [95% CI -3.522 to -0.533]; P = 0.008; CDR-SB difference of -0.483 [95% CI -0.853 to -0.114]; P = 0.010; ADCS-ADL difference of 0.775 [95%CI -0.874 to 2.423]; P = 0.357). Plasma Aβ42/40-ratio increased significantly with blarcamesine group vs. placebo, (P = 0.048) and whole brain volume loss was significantly decreased (P = 0.002). Participants in the full safety population with ≥1 serious treatment-emergent adverse events (TEAEs) occurred in 56 participants (16.7%) in the blarcamesine and 17 (10.1%) in the placebo group. Common TEAEs included dizziness, which was transient and mostly mild to moderate in severity. One death in the blarcamesine group and 1 in the placebo group were both not considered treatment related., Conclusions: Blarcamesine, demonstrating a safety profile with no associated neuroimaging adverse events, significantly slowed clinical progression by 36.3% at 48 weeks with blarcamesine group as well as the individual 30 mg (by 34.6%) and 50 mg (by 38.5%) blarcamesine groups vs. placebo on the prespecified primary cognitive endpoint ADAS-Cog13. The prespecified secondary endpoint CDR-SB, which is used as the sole primary endpoint in recent successful AD drug submissions, is significantly improved at Week 48 with blarcamesine relative to placebo. The findings are supported by biomarkers from the A/T/N spectrum, including plasma Aβ42/40-ratio and reduction of whole brain atrophy. Additionally, the prespecified SIGMAR1 gene variant subgroup analysis confirmed beneficial clinical effect of blarcamesine group through upstream SIGMAR1 activation - subjects with the common SIGMAR1 wild-type gene (excluding carriers of the mutated SIGMAR1 rs1800866 variant) experienced an even greater significant clinical benefit with slowed clinical progression by 49.8% at 48 weeks on the prespecified primary cognitive endpoint ADAS-Cog13. Oral once daily blarcamesine could represent a novel treatment in early AD and be complementary or alternative to anti-beta amyloid drugs., Competing Interests: Declaration of competing interest DISCLOSURES: Dr. Sabbagh discloses ownership interest (stock or stock options) in NeuroTau, Inc., uMETHOD, Athira Pharma, Inc., and CervoMed and Lighthouse Pharmaceuticals; consulting for Alzheon, Inc, Genentech (Roche Group), Prothena, Novo Nordisk, Anavex Life Sciences, T3D Therapeutics, Inc., Eisai Co., Ltd., Eli Lilly and Co., and KeifeRx. Dr. Macfarlane has received paid honoraria from the following pharmaceutical companies for various speaking engagements and advisory board services: Eisai, Eli Lilly, Janssen-Cilag, Lundbeck, Novo Nordisk. Dr. Macfarlane is contracted by Anavex Life Sciences to provide medical monitoring services for Anavex's Rett syndrome studies. Dr. Grimmer received consulting fees from AbbVie, Alector, Anavex Life Sciences, Biogen, Cogthera, Eli Lilly, Functional Neuromodulation, Grifols, Iqvia, Janssen, Noselab, Novo Nordisk, NuiCare, Orphanzyme, Roche Diagnostics, Roche Pharma, UCB, and Vivoryon; lecture fees from Biogen, Eisai, Grifols, Medical Tribune, Novo Nordisk, Roche Pharma, Schwabe, and Synlab; and has received grants to his institution from Biogen, Eisai, and Roche Diagnostics. Dr. O'Brien's institution has received consultancy and/research funding from Anavex Life Sciences, Eisai, UCB Pharma, ES Therapeutics, Kinoxis Pharmaceuticals, Supernus, Autobahn, Shanghai Zhimeng, Epidarex, and government grant funding from NHMRC (APP1176426), MRFF, DoD and NINDS. Dr. Woodward has received honoraria for speaking and expert advice from Actinogen, Biogen, Roche, MSD/Merck, Glaxo Smith Kline, Cognition Therapies, Eisai, Novo Nordisk and Pfizer. He was previously paid for his role as Chief National Investigator for Anavex Life Sciences. He owns no shares and has no direct employment with any pharmaceutical company or Biotech. Dr. Tartaglia is SAB member of Brain Injury Canada, PSP Awareness, and Women's Brain Project. Advisory to Eisai, Eli Lilly and QurAlis and received Grant funding from NIH, Weston Brain Institute, Tanenbaum Institute for Science in Sport and participated in clinical trials: Biogen, Novo Nordisk, Janssen, Roche, Anavex Life Sciences, Passage Bio, Green Valley. Dr. Frank received paid honoraria from the following pharmaceutical companies for advisory board services: Eisai, Eli Lilly, Roche Pharma, Novo Nordisk. Dr. Lai has received a paid honorarium for speaking engagements with INmune Bio. Dr. Lewis is supported by a National Health and Medical Research Council Leadership Fellowship (1195830) and has received research funding from The Michael J. Fox Foundation and the Australian Research Council, as well as consulting for Pharmaxis Ltd. Dr. Kurrle has received honoraria for educational activities from Roche Diagnostics and Novartis. Dr. Cohen discloses consulting work (no personal fees received) for: Alnylam, Biogen, Biohaven, Cassava, Cogstate, Cognivue, Eisai, Eli Lilly, INmune Bio, Novo Nordisk, ProMIS Neuroscience, Roche, RetiSpec, SciNeuro; and research grants (paid to institution only) from: AbbVie, AgeneBio, Alector, Alnylam, Alzheon, Anavex Life Sciences, Biogen, Cassava, Eisai, Eli Lilly, Janssen, Novo Nordisk, Roche, RetiSpec, UCB Biopharma. Dr. Grunfeld has received paid honoraria from the Janssen-Cilag for advisory board services. Dr. Morris has no financial conflicts of interest to declare. Dr. Connell does not have any professional conflicts of interest. Dr. Thompson does not have any conflicts of interests to declare. Dr. Tacik does not have any conflicts of interests to declare. Dr. Perry has received paid honoraria from the following pharmaceutical companies for various speaking engagements and advisory board services: Eisai, Eli Lilly, MSDF, Biogen, and Roche. Dr. Sharif does not have any conflicts of interest to disclose. Dr. Kalafatis does not have any conflicts of interests to declare. Dr. Munisamy does not have any conflicts of interests to declare. Dr. Pearson has received paid honoraria for speaking and advice from Biogen, Eli Lilly and Boehringer-Ingelheim. Dr. Sturm does not have any conflicts of interests to declare. Dr. Oschmann received research support as well as speaking fees and travel fees from Alexion, Bayer Health Care, Biogen, Janssen, Merck Serono, Novartis, Pfizer, Roche, Sanofi Genzyme, TEVA. Dr. Hsiung discloses that he has received grants or contracts from CIHR, NIA/NIH and has participated in expert advisory committee supported by Biogen, Roche, and NovoNordisk. Dr. Hsiung is the current president of C5R (Consortium of Canadian Centres for Clinical Cognitive Research). Dr. Lynch does not have any conflicts of interests to declare. Dr. Brew does not have any conflicts of interests to declare. Dr. Tucker is employed by Anavex Life Sciences as an independent consultant to provide medical monitoring services for the Alzheimer's disease program. Dr. Ingram discloses no financial ownership interest in any pharmaceutical company but has been paid honoraria by Eisai, Merck, Biogen, Roche, Janssen, Eli Lilly to participate in health care planning and messaging regarding their products’ impact on dementia. Anavex research responsibilities were contractually held by Kawartha Centre ∼ Redefining Healthy Aging, previously owned by Dr. Ingram. This company has changed ownership as of January 5, 2023. Dr. Pasternak has received grant support to his institution and hold shares in Zywie Bio LLC. He has received speakers fees from Eli Lilly. Dr. MacSweeney does not have any conflicts of interests to declare. Dr. Short has received paid honoraria from Roche and Eisai for Advisory Board services and speaking engagements. Dr. Bhatt does not have any conflicts of interests to declare. Dr. Drysdale discloses that he has been paid for conducted research by the following companies, Eli Lilly, Cassava Sciences, Roche, Anavex Life Sciences, Lundbeck and Biogen. Dr. Mannering does not have any conflicts of interests to declare. Dr. Henri-Bhargava has received paid honoraria for Advisory boards / speaking engagements for Roche, Lilly, Eisai, Boehringer Ingelheim; Clinical trial payments from: Lilly, Roche, Boheringer Ingelhiem, Anavex Life Sciences, Cerevel, Green Valley Shanghai, Intelgenx; Grants from Canadian Consortium on Neurodegeneration in Aging, Centre for Aging and Brain Health Innovation, Manning Cognitive Health Initiative. Dr. Froelich has received honoraria for consulting or presentations from Biogen, BioVie, Eisai, Grifols, Janssen Cilag, Neurimmune, Noselab, NovoNordisk, Roche, TauRX, Schwabe; Honoraria for Clinical study committees from Avanir/Otsuka, PharmatrophiX, Charité Berlin, Neuroscios, Vivoryon; Clinical trials (honoraria to his institution) from Axon Neuroscience, Anavex Life Sciences, Alector, Boehringer Ingelheim, Eisai, Hummingbird, NovoNordisk, Noselab. Dr. Chertkow has been supported by a Foundation Grant from the CIHR (Canadian Institutes for Health Research), along with funding from the National Institute of Health (US), the Weston Foundation and the Baycrest Health Sciences Foundation. He has participated as a site PI in pharmaceutical trial activities sponsored by Hoffmann-La Roche, TauRx, Lilly, Anavex Life Sciences, Alector, Biogen, Esai, and Immunocal (site investigator for trials). He has participated as an unpaid advisor in 2020 for establishment of an international database by Biogen. He has participated in advisory boards for Esai and Lilly Co., with honoraria going to the Rotman Research Institute. He is Scientific Director for the CCNA, which receives partner support from partners including Pfizer, Lilly, Sanofi. Dr. Mander does not have any conflicts of interests to declare. Dr. Wiltfang does not have any conflicts of interests to declare. Dr. Prins performed consultancy work for Aribio, Amylyx, Eli-Lilly and Janssen and received a speaker fee from Biogen. He is co‐PI of of a current trial with Fuji Film Toyama Chemical. He is CEO and co‐owner of Brain Research Center, the Netherlands. Dr. Peters received consulting or lecture fees from Biogen, Eisai, Eli Lilly, Grifols, Medical Tribune, Noselab, Novo Nordisk, Prinnovation, Priavoid, Roche Diagnostics and Roche Pharma; and has received grants to his institution from Biogen, Eisai, Eli Lilly, Noslab, Predemtec, Roche Pharma, Roche Diagnostics and Vivoryon. Dr. Smith has received personal consulting fees from Alnylam Pharmaceuticals and Eli Lilly. Dr. Dautzenberg has participated as PI in pharmaceutical trials activities sponsored by TauRx, Lilly, Anavex Life Sciences, Alector, Biogen Boehringer Ingelheim, Eisai, NovoNordisk, Green Valley Shanghai, Roche and received a speaker fee from NovoNordisk as National PI. Dr. Evans does not have any conflicts of interests to declare. Dr. Villa does not have any conflicts of interests to declare. Dr. Gordon does not have any conflicts of interests to declare. Dr. Jubault does not have any conflicts of interests to declare. Dr. Guizard does not have any conflicts of interests to declare. Dr. Kaufmann discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Kun Jin discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Chezem discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Missling discloses being an employee of and ownership interest (stock or stock options) in Anavex Life Sciences. Dr. Babajide does not have any conflicts of interest to declare. Dr. Brodtmann has received paid honoraria from the following pharmaceutical companies for advisory board services: Biogen, Roche and Eisai. Dr. Asher does not have any conflicts of interests to declare., (Copyright © 2024 Anavex Life Sciences Corp. Published by Elsevier Masson SAS.. All rights reserved.)
- Published
- 2025
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