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Robitaille, Nicolas

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Robitaille

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Nicolas

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Université Laval

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ncf11924430

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  • PublicationAccès libre
    Toward dynamic pain expressions in avatars : perceived realism and pain level of diffrent action unit orders
    (Elsevier, 2019-02-07) Jackson, Philip L.; Robitaille, Nicolas; Tessier, Marie-Hélène; Gingras, Chloé
    The facial expression of pain can be decomposed in three sets of Action Units (AUs), the smallest discriminating facial movements: Brow lowering (B), Nose wrinkling + Upper lip raising (N), and Orbit tightening + Eyelid closure (O). This study compared the perception of realism and pain level from different onset orders of AUs in avatars. Seven videos of facial expressions of pain were created with four different avatars (2 women): six sequential onsets combining the three sets of AUs and one synchronized onset. 45 healthy adults (22 women; aged 23.6 ± 5.2 years) rated the realism of facial movements, and the level of intensity and unpleasantness of perceived pain. A more realistic expression was associated with the onset of O before or at the same time as N, a more intense expression was associated when B occurred last, and a higher level of unpleasantness was associated with the onset of N before B. Therefore, the sequence ONB yielded the highest ratings on both measures of realism and pain levels. These findings describe the perceived content of different orders of facial movements that could contribute to the creation of realistic pain-expressing virtual agents designed to study human-computer interactions.
  • PublicationAccès libre
    High-dimensional medial lobe morphometry : an automated MRI biomarker for the new AD diagnostic criteria
    (Hindawi, 2014-08-31) Robitaille, Nicolas; Valdivia, Fernando; Mouiha, Abderazzak; Duchesne, Simon
    Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer’s disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. The study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer’s Disease Neuroimaging Initiative, with data acquired on 58 different 1.5 T scanners. We measured the sensitivity and specificity of our technique in a hierarchical fashion, first testing the effect of intensity standardization, then between different volumes of interest, and finally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. The positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique’s ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD.
  • PublicationAccès libre
    A virtual reality avatar interaction (VRai) platform to assess residual executive dysfunction in active military personnel with previous mild traumatic brain injury : proof of concept
    (Taylor & Francis Informa Healthcare, 2016-09-08) Jackson, Philip L.; Mercier, Catherine; Hébert, Luc J.; Robitaille, Nicolas; Richards, Carol Lillian; Fecteau, Shirley; Bouyer, Laurent; McFadyen, Bradford James
    Purpose: This proof of concept study tested the ability of a dual task walking protocol using a recently developed avatar-based virtual reality (VR) platform to detect differences between military personnel post mild traumatic brain injury (mTBI) and healthy controls. Methods: The VR platform coordinated motion capture, an interaction and rendering system, and a projection system to present first (participant-controlled) and third person avatars within the context of a specific military patrol scene. A divided attention task was also added. A healthy control group was compared to a group with previous mTBI (both groups comprised of six military personnel) and a repeated measures ANOVA tested for differences between conditions and groups based on recognition errors, walking speed and fluidity and obstacle clearance. Results: The VR platform was well tolerated by both groups. Walking fluidity was degraded for the control group within the more complex navigational dual tasking involving avatars, and appeared greatest in the dual tasking with the interacting avatar. This navigational behaviour was not seen in the mTBI group. Conclusions: The present findings show proof of concept for using avatars, particularly more interactive avatars, to expose differences in executive functioning when applying context-specific protocols (here for the military). Implications for rehabilitation Virtual reality provides a means to control context-specific factors for assessment and intervention. Adding human interaction and agency through avatars increases the ecologic nature of the virtual environment. Avatars in the present application of the Virtual Reality avatar interaction platform appear to provide a better ability to reveal differences between trained, military personal with and without mTBI.
  • PublicationAccès libre
    Test-retest Reliability of a new medial temporal atrophy morphological metric
    (Hindawi, 2012-09-17) Robitaille, Nicolas; Valdivia, Fernando; Mouiha, Abderazzak; Duchesne, Simon
    Clinicians and researchers alike are in need of quantitative and robust measurement tools to assess medial temporal lobe atrophy (MTA) due to Alzheimer’s disease (AD). We recently proposed a morphological metric, extracted from T1-weighted magnetic resonance images (MRI), to track and estimate MTA in cohorts of controls, AD, and mild cognitive impairment subjects, at high-risk of progression to dementia. In this paper, we investigated its reliability through analysis of within-session scan/repeat images and scan/rescans from large multicenter studies. In total, we used MRI data from 1051 subjects recruited at over 60 centers. We processed the data identically and calculated our metric for each individual, based on the concept of distance in a high-dimensional space of intensity and shape characteristics. Over 759 subjects, the scan/repeat change in the mean was 1.97% (SD: 21.2%). Over three subjects, the scan/rescan change in the mean was 0.89% (SD: 22.1%). At this level, the minimum trial size required to detect this difference is 68 individuals for both samples. Our scan/repeat and scan/rescan results demonstrate that our MTA assessment metric shows high reliability, a necessary component of validity.
  • PublicationAccès libre
    Tissue-based MRI intensity standardization : application to multicentric datasets
    (Hindawi Pub. Corp., 2012-05-03) Robitaille, Nicolas; Mouiha, Abderazzak; Crépeault, Burt; Duchesne, Simon; Valdivia, Fernando
    Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.
  • PublicationAccès libre
    Label fusion strategy selection
    (Hindawi Pub. Corp., 2012-02-06) Robitaille, Nicolas; Duchesne, Simon
    Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.