Dr. Patrick Desrosiers Profile

Dr. Patrick Desrosiers

Research Professor at Institut Universitaire en Santé Mentale de Québec

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Publications (4)

Proceedings Article | 13 March 2024 Presentation

Development of a label-free optofluidic platform based on quantitative-phase digital holographic microscopy and microfluidics to perform biophysical phenotyping of human cells

Erik Bélanger, Gabrielle Jess, Céline Larivière-Loiselle, Sara Mattar, Niraj Patel, Zahra Yazdani-Najafabadi, Mohamed Haouat, Johan Chaniot, Émile Rioux-Pellerin, Marie-Ève Crochetière, Jean-Xavier Giroux, Antoine Allard, Patrick Desrosiers, Pierre Marquet

Proceedings Volume PC12837, PC128370D (2024) https://doi.org/10.1117/12.3002808

KEYWORDS: Digital holography, Microfluidics, Cell phenotyping, Optofluidics, Microscopy, Holography, Refractive index, Microfluidic imaging, Imaging devices, Velocimetry

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SPIE Journal Paper | 30 September 2023 Open Access

Zebrafish brain atlases: a collective effort for a tiny vertebrate brain

Antoine Légaré, Mado Lemieux, Patrick Desrosiers, Paul De Koninck

NPh, Vol. 10, Issue 04, 044409, (September 2023) https://doi.org/10.1117/12.10.1117/1.NPh.10.4.044409

KEYWORDS: Brain, Neuroimaging, Brain mapping, Data modeling, Calcium, Anatomy, Biological imaging, Neuroscience, Databases, Confocal microscopy

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SPIE Journal Paper | 7 April 2022 Open Access

Toward an integrative neurovascular framework for studying brain networks

Jeremie Guilbert, Antoine Légaré, Paul De Koninck, Patrick Desrosiers, Michèle Desjardins

NPh, Vol. 9, Issue 03, 032211, (April 2022) https://doi.org/10.1117/12.10.1117/1.NPh.9.3.032211

KEYWORDS: Brain, Functional magnetic resonance imaging, Neuroimaging, Calcium, Neurons, Neurophotonics, Blood, Neurovascular coupling, Hemodynamics, Data modeling

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Proceedings Article | 26 April 2016 Presentation

Network inference from functional experimental data (Conference Presentation)

Patrick Desrosiers, Simon Labrecque, Maxime Tremblay, Mathieu Bélanger, Bertrand De Dorlodot, Daniel Côté

Proceedings Volume 9690, 969019 (2016) https://doi.org/10.1117/12.2212726

KEYWORDS: Neurons, Calcium, Luminescence, Brain mapping, Image segmentation, Microscopy, Statistical analysis, Statistical methods, Information theory, Computer simulations

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Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic conditions.

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