@article{ansado_how_2021,

title = {How brain imaging provides predictive biomarkers for therapeutic success in the context of virtual reality cognitive training},

author = {J. Ansado and C. Chasen and S. Bouchard and G. Northoff},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089070480&doi=10.1016%2fj.neubiorev.2020.05.018&partnerID=40&md5=8e5ea676958e050b32a893830dbc2a93},

doi = {10.1016/j.neubiorev.2020.05.018},

issn = {01497634},

year  = {2021},

date = {2021-01-01},

journal = {Neuroscience and Biobehavioral Reviews},

volume = {120},

pages = {583–594},

abstract = {As Virtual reality (VR) is increasingly used in neurological disorders such as stroke, traumatic brain injury, or attention deficit disorder, the question of how it impacts the brain's neuronal activity and function becomes essential. VR can be combined with neuroimaging to offer invaluable insight into how the targeted brain areas respond to stimulation during neurorehabilitation training. That, in turn, could eventually serve as a predictive marker for therapeutic success. Functional magnetic resonance imaging (fMRI) identified neuronal activity related to blood flow to reveal with a high spatial resolution how activation patterns change, and restructuring occurs after VR training. Portable and quiet, electroencephalography (EEG) conveniently allows the clinician to track spontaneous electrical brain activity in high temporal resolution. Then, functional near-infrared spectroscopy (fNIRS) combines the spatial precision level of fMRIs with the portability and high temporal resolution of EEG to constitute an ideal measuring tool in virtual environments (VEs). This narrative review explores the role of VR and concurrent neuroimaging in cognitive rehabilitation. © 2020 Elsevier Ltd},

note = {Publisher: Elsevier Ltd},

keywords = {accuracy, attention deficit disorder, biological monitoring, brain depth stimulation, brain electrophysiology, brain radiography, brain region, cell function, cerebrovascular accident, clinician, cognition, Cognitive rehabilitation, disease marker, electroencephalogram, Electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy, human, image quality, in vivo study, nerve cell, neuroimaging, neurologic disease, neuropsychological test, neuropsychology, neurorehabilitation, nonhuman, prediction, priority journal, review, therapy effect, training, traumatic brain injury, virtual reality, virtual reality cognitive training},

pubstate = {published},

tppubtype = {article}

}

@article{allili_likelihood-based_2015,

title = {Likelihood-based feature relevance for figure-ground segmentation in images and videos},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952631642&doi=10.1016%2fj.neucom.2015.04.015&partnerID=40&md5=833948d0784e0dc42c2245b9343971dd},

doi = {10.1016/j.neucom.2015.04.015},

issn = {09252312},

year  = {2015},

date = {2015-01-01},

journal = {Neurocomputing},

volume = {167},

pages = {658–670},

abstract = {We propose an efficient method for image/video figure-ground segmentation using feature relevance (FR) and active contours. Given a set of positive and negative examples of a specific foreground (an object of interest (OOI) in an image or a tracked objet in a video), we first learn the foreground distribution model and its characteristic features that best discriminate it from its contextual background. For this goal, an objective function based on feature likelihood ratio is proposed for supervised FR computation. FR is then incorporated in foreground segmentation of new images and videos using level sets and energy minimization. We show the effectiveness of our approach on several examples of image/video figure-ground segmentation. © 2015 Elsevier B.V.},

note = {Publisher: Elsevier},

keywords = {accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording},

pubstate = {published},

tppubtype = {article}

}