Stephane Doyen

Stephane Doyen
Omniscient Neurotechnology · Data Science

PhD
Chief Data Scientist

About

37
Publications
10,201
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1,245
Citations

Publications

Publications (37)
Chapter
In this chapter, we introduce the topic of the brain connectome, consisting of the complete set of both the structural and functional connections of the brain. Connectomic information and the large-scale network architecture of the brain provide an improved understanding of the organization and functional relevance of human cortical and subcortical...
Article
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The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural conne...
Article
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How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno‐occlusive vasculopathy (CSOV) to map both ischemic symptoms...
Article
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Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed impor...
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Judgement is a higher-order brain function utilized in the evaluation process of problem solving. However, heterogeneity in the task methodology based on the many definitions of judgement and its expansive and nuanced applications have prevented the identification of a unified cortical model at a level of granularity necessary for clinical translat...
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Background Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and t...
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Introduction: Data-driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom-specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on us...
Article
Background: Locating the hand-motor-cortex (HMC) is an essential component within many neurosurgeries. Despite advancements in these localization methods there are still downfalls for each. Additionally, the importance of presurgical planning calls for increasingly accurate and efficient methods of locating specific cortical regions. Objective:...
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Objective Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify marke...
Article
Background: Despite efforts to improve targeting accuracy of the dorsolateral prefrontal cortex (DLPFC) as a repetitive transcranial magnetic stimulation (rTMS) target for Major Depressive Disorder (MDD), the heterogeneity in clinical response remains unexplained. Objective: We sought to compare the patterns of functional connectivity from the D...
Article
Objective: This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology. Methods: T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 1...
Article
Increasing data suggests major depressive disorder (MDD) involves abnormal functional connectivity within a variety of large-scale brain networks. However, due to the use of unstandardized parcellation schemes, the interactions between these networks and the specific neuroanatomic substrates involved requires further review. We therefore sought to...
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Resection of infiltrating brain tumors, such as diffuse gliomas, generally involves resecting a portion of a lobe of the brain. While the concept of localized functions, and the risk to removing these areas, is well established in neurosurgical thinking, the potential that the overall global efficiency of the connectome could be disproportionately...
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Objective Progressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectori...
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Objective: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy. Materials and m...
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The Gerstmann syndrome is a constellation of neurological deficits that include agraphia, acalculia, left-right discrimination and finger agnosia. Despite a growing interest in this clinical phenomenon, there remains controversy regarding the specific neuroanatomic substrates involved. Advancements in data-driven, computational modeling provides an...
Preprint
Full-text available
Despite efforts to improve targeting of the dorsolateral prefrontal cortex (DLPFC) in repetitive transcranial magnetic stimulation (rTMS) for Major Depressive Disorder (MDD), the heterogeneity in clinical response remains unexplained. We compared patterns of functional connectivity from the DLPFC in patients with MDD between responders and non-resp...
Article
Full-text available
The healthcare field has long been promised a number of exciting and powerful applications of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI techniques, such as machine learning (ML), have proven the ability to model enormous amounts of complex data and biological phenomena in ways only imaginable with...
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Purpose Applying graph theory to the human brain has the potential to help prognosticate the impacts of intracerebral surgery. Eigenvector (EC) and PageRank (PR) centrality are two related, but uniquely different measures of nodal centrality which may be utilized together to reveal varying neuroanatomical characteristics of the brain connectome. M...
Article
COVER ILLUSTRATION The Structural Connectivity Atlas (SCA). Using the distribution of white matter tracts throughout the brain, an automated algorithm creates a personalized brain atlas comprised of 379 parcels unsusceptible to structural deformity or inter‐individual differences.
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For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology...
Article
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Repetitive transcranial magnetic stimulation (rTMS) is a promising approach for post-stroke rehabilitation but there lacks a rationale strategy to plan, execute, and monitor treatment. We present a case of targeted rTMS using the Omniscient Infinitome software to devise targets for treatment in a post-stroke patient and describe the functional conn...
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Purpose Current limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigating both the magnitude and directionality of various features on the classification into a positive or...
Article
Background Neurosurgeons have limited tools in their armamentarium to visualize critical brain networks during surgical planning. Quicktome was designed using machine-learning to generate robust visualization of important brain networks that can be used with standard neuro-navigation to minimize those deficits. We sought to see whether Quicktome co...
Preprint
Full-text available
Current limitations in boosted tree modelling prevent the effective scaling to datasets with a large feature number, particularly when investigating the magnitude and directionality of various features on classification. We present a novel methodology, Hollow-tree Super (HOTS), to resolve and visualize feature importance in boosted tree models invo...
Article
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Introduction: The semantic network is an important mediator of language, enabling both speech production and the comprehension of multimodal stimuli. A major challenge in the field of neurosurgery is preventing semantic deficits. Multiple cortical areas have been linked to semantic processing, though knowledge of network connectivity has lacked an...
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Introduction Understanding the human connectome by parcellations allows neurosurgeons to foretell the potential effects of lesioning parts of the brain during intracerebral surgery. However, it is unclear whether there exist variations among individuals such that brain regions that are thought to be dispensable may serve as important networking hub...
Article
Over the past several years, two largely separate traditions have collided, leading to controversy over claims about priming. We describe and contrast the main accounts of priming effects in cognitive and social psychology, focusing especially on the role of awareness. In so doing, we consider one of the core points of contention: claims about the...
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This article revisits two classical issues in experimental methodology: experimenter bias and demand characteristics. We report a content analysis of the method section of experiments reported in two psychology journals (Psychological Science and the Journal of Personality and Social Psychology), focusing on aspects of the procedure associated with...
Article
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The perspective that behavior is often driven by unconscious determinants has become widespread in social psychology. Bargh, Chen, and Burrows' (1996) famous study, in which participants unwittingly exposed to the stereotype of age walked slower when exiting the laboratory, was instrumental in defining this perspective. Here, we present two experim...
Data
list of stimuli. Here is the list of the primes and neutral words as well as their English translation used in both Experiment 1 and 2. (DOCX)
Article
Change blindness-our inability to detect changes in a stimulus-occurs even when the change takes place gradually, without any disruption [Simons, D. J., Franconeri, S. L., & Reimer, R. L. (2000). Change blindness in the absence of a visual disruption. Perception, 29(10), 1143-1154]. Such gradual changes are more difficult to detect than changes tha...

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