@article{bogie_using_2024,

title = {Using virtual reality to improve verbal episodic memory in schizophrenia: A proof-of-concept trial},

author = {B. J. M. Bogie and C. Noël and F. Gu and S. Nadeau and C. Shvetz and H. Khan and M. -C. Rivard and S. Bouchard and M. Lepage and S. Guimond},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186986986&doi=10.1016%2fj.scog.2024.100305&partnerID=40&md5=a15c598b45b8f44a40b25fe5fd078a06},

doi = {10.1016/j.scog.2024.100305},

issn = {22150013 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Schizophrenia Research: Cognition},

volume = {36},

abstract = {Background: Schizophrenia is associated with impairments in verbal episodic memory. Strategy for Semantic Association Memory (SESAME) training represents a promising cognitive remediation program to improve verbal episodic memory. Virtual reality (VR) may be a novel tool to increase the ecological validity and transfer of learned skills of traditional cognitive remediation programs. The present proof-of-concept study aimed to assess the feasibility, acceptability, and preliminary efficacy of a VR-based cognitive remediation module inspired by SESAME principles to improve the use of verbal episodic memory strategies in schizophrenia. Methods: Thirty individuals with schizophrenia/schizoaffective disorder completed this study. Participants were randomized to either a VR-based verbal episodic memory training condition inspired by SESAME principles (intervention group) or an active control condition (control group). In the training condition, a coach taught semantic encoding strategies (active rehearsal and semantic clustering) to help participants remember restaurant orders in VR. In the active control condition, participants completed visuospatial puzzles in VR. Attrition rate, participant experience ratings, and cybersickness questionnaires were used to assess feasibility and acceptability. Trial 1 of the Hopkins Verbal Learning Test – Revised was administered pre- and post-intervention to assess preliminary efficacy. Results: Feasibility was demonstrated by a low attrition rate (5.88 %), and acceptability was demonstrated by limited cybersickness and high levels of enjoyment. Although the increase in the number of semantic clusters used following the module did not reach conventional levels of statistical significance in the intervention group, it demonstrated a notable trend with a medium effect size (t = 1.48},

note = {Publisher: Elsevier Inc.},

keywords = {adult, article, clinical article, clinical assessment, Cognitive remediation therapy, cybersickness, disease severity, dizziness, Ecological treatment, Episodic memory, exclusion VR criteria questionnaire, feasibility study, female, Hopkins verbal learning test, human, male, mini international neuropsychiatric interview, nausea, outcome assessment, Positive and Negative Syndrome Scale, Proof of concept, questionnaire, randomized controlled trial, schizophrenia, scoring system, Semantic encoding, Semantics, task performance, training, Verbal memory, virtual reality, vr experience questionnaire},

pubstate = {published},

tppubtype = {article}

}

@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{ansado_virtual_2018,

title = {The virtual reality working-memory-training program (VR WORK M): Description of an individualized, integrated program},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067881079&partnerID=40&md5=c964c5af28aa91128bdeaa0b9b89e645},

issn = {15548716},

year  = {2018},

date = {2018-01-01},

journal = {Annual Review of CyberTherapy and Telemedicine},

volume = {2018},

number = {16},

pages = {101–117},

abstract = {Working memory (WM), which allows us to retain information in memory during a complex task, is a cognitive function that is crucial to daily life. It can be affected by several neurological conditions, such as traumatic brain injury or stroke. Numerous studies suggest that it is possible to resolve WM deficits using targeted rehabilitation programs. Virtual reality (VR) is an innovative technology that has proven to be valuable in the evaluation and rehabilitation of cognitive functions. It potentially optimizes cognitive stimulation in a safe environment and can help improve functional activities of daily living by replicating real-life scenarios. With that in mind, this article introduces the first VR-based WM rehabilitation program. The WM training program (Virtual Reality Working-Memory-Training program, VR-WORK-M) recreates a restaurant environment where participants must complete a WM task consisting in repeating a series of items heard via a headset. The goal is to train WM by simulating a business proposal presentation. The program contains several levels of difficulty resulting from the combination of four complexity factors: (1) the type of business concerned by the proposal (e.g., opening a bakery vs. opening a flower shop); (2) the number of items to repeat (4 vs. 5 vs. 6); (3) the number of subtasks to complete before the WM task (e.g., introducing oneself vs order a drink); and (4) the modality of distractors (e.g., an auditory distractor vs. a visual distractor). VR-WORK-M includes 54 levels of difficulty to be administered in a training program over a period of four weeks, with four or five sessions per week. © 2018, Interactive Media Institute. All rights reserved.},

note = {Publisher: Interactive Media Institute},

keywords = {article, cognition, daily life activity, human, rehabilitation, task performance, training, virtual reality, working memory},

pubstate = {published},

tppubtype = {article}

}