Biology subjects

Theberge, A.

Publications and source records attributed to Theberge, A..

6 recordsLinked to original sources

bioRxiv2026

Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc

Diffusion MRI enables noninvasive mapping of human white matter pathways, but automated segmentation methods have largely focused on large association, projection, and commissural bundles. Brainstem and subcortical pathways supporting basal ganglia, cerebellar, limbic/reward, sensory, and homeostatic functions remain underrepresented in large-scale connectomic analyses. To address this gap, we adapted BundleParc, a recently introduced bundle-parcellation architecture, into an automated pipeline for direct segmentation and along-tract parcellation of 97 subcortical and brainstem white matter pathways. The model was trained on a curated reference dataset derived from Human Connectome Project diffusion MRI using anatomy-guided tractography, explicit inclusion and exclusion criteria, automated outlier filtering, and manual quality assurance. Operating directly on native-space fiber orientation distributions, the algorithm successfully recovers these intricate anatomical trajectories and ordered parcellations. We show the model generalizes to diverse external datasets spanning development, aging, and neurodegenerative disease cohorts, maintaining robust performance across variations in spatial resolution and angular sampling. The released container, trained model, population atlas, reference streamlines, and quality assurance outputs provide a resource for studying deep brainstem and subcortical pathways in development, aging, disease, and neuromodulation-relevant anatomy.

neuroscience↗

bioRxiv2026

From Fabrication to Flow: Impact of Print Orientation on Surface Qualities and Capillary-Driven Flow in Laser SLA-based Open Microchannels

Stereolithography (SLA) 3D printing has become increasingly popular for fabricating microfluidic devices, with applications including hydrogel patterning and tissue modeling. In open-channel systems with surface tension-driven flow, 3D-printer-induced discrepancies in channel surface texture can significantly impact fluid flow and device performance. While previous work has focused on comparing different 3D printing methods for microchannel fabrication, the effect of device orientation during SLA printing on microchannel morphology and capillary-driven flow has not been systematically evaluated. Furthermore, there is minimal research elucidating the influence of channel surface texture on the flow of biologically relevant hydrogel precursors commonly used in organ-on-a-chip applications. Herein, we investigated the impact of print orientation on channel morphology, fluid wetting behavior, and fluid flow by comparing laser SLA-based parts where the length of the channel was tilted at 0{degrees}, 15{degrees}, 45{degrees}, or 90{degrees} during printing. We demonstrated that channel floor surface texture is greatly affected by print orientation: the highest axial surface roughness was measured in 15{degrees} printed channels, and the highest axial surface tortuosity-which describes the real length along the surface-was measured in 45{degrees} printed channels. Print angles of 15{degrees} and 45{degrees} also resulted in asymmetric roughness of the channel floor, which caused asymmetric wetting of glycerol solution. Surface tension-driven flow of glycerol solution, agarose precursor solution, and collagen precursor solution was affected by print orientation, in which the 45{degrees} printed flow devices had slowest flow for all test fluids. Root mean square roughness was not a reliable predictor of slower flow; instead, surface tortuosity should be considered. Potential alternatives to better theoretically model how print angle-induced surface texture affects open-channel flow are discussed as well. These findings provide a framework of fabrication considerations for laser SLA printing of open microchannels that can also be applied to other layer-by-layer, vat photopolymerization-based 3D printing technologies.

bioengineering↗

bioRxiv2022

High-frequency longitudinal white matter diffusion- & myelin-based MRI database: reliability and variability

Assessing the consistency of quantitative MRI measurements is critical for inclusion in longitudinal studies and clinical trials. Intraclass coefficient correlation and coefficient of variation were used to evaluate the different consistency aspects of diffusion- and myelinbased MRI measures. Multi-shell diffusion and inhomogeneous magnetization transfer datasets were collected from twenty healthy adults at a high-frequency of five MRI sessions. The consistency was evaluated across whole bundles and the track-profile along the bundles. The impact of the fiber populations on the consistency was also evaluated using the number of fiber orientations map. For whole and profile bundles, moderate to high reliability of diffusion and myelin measures were observed. We report higher reliability of measures for multiple fiber populations than single. The overall portrait of the most consistent measurements and bundles drawn from a wide range of MRI techniques presented here will be particularly useful for identifying reliable biomarkers capable of detecting, monitoring and predicting white matter changes in clinical applications and has the potential to inform patient-specific treatment strategies. Key pointsO_LIReliability and variability are excellent to good for DWI measurements, and good to moderate for MT measures for whole bundles and along the bundles. C_LIO_LIThe number of fiber populations affects the reliability and variability of the MRI measurements. C_LIO_LIThe reliability and variability of MRI measurements are also bundle dependent. C_LI

neuroscience↗

bioRxiv2020

Track-To-Learn: A general framework for tractography with deep reinforcement learning

AO_SCPLOWBSTRACTC_SCPLOWDiffusion MRI tractography is currently the only non-invasive tool able to assess the white-matter structural connectivity of a brain. Since its inception, it has been widely documented that tractography is prone to producing erroneous tracks while missing true positive connections. Anatomical priors have been conceived and implemented in classical algorithms to try and tackle these issues, yet problems still remain and the conception and validation of these priors is very challenging. Recently, supervised learning algorithms have been proposed to learn the tracking procedure implicitly from data, without relying on anatomical priors. However, these methods rely on labelled data that is very hard to obtain. To remove the need for such data but still leverage the expressiveness of neural networks, we introduce Track-To-Learn: A general framework to pose tractography as a deep reinforcement learning problem. Deep reinforcement learning is a type of machine learning that does not depend on ground-truth data but rather on the concept of "reward". We implement and train algorithms to maximize returns from a reward function based on the alignment of streamlines with principal directions extracted from diffusion data. We show that competitive results can be obtained on known data and that the algorithms are able to generalize far better to new, unseen data, than prior machine learning-based tractography algorithms. To the best of our knowledge, this is the first successful use of deep reinforcement learning for tractography.

neuroscience↗

bioRxiv2019

Injection molded open microfluidic well plate inserts for user-friendly coculture and microscopy

Open microfluidic cell culture systems are powerful tools for interrogating biological mechanisms. We have previously presented a microscale cell culture system, based on spontaneous capillary flow of biocompatible hydrogels, that is integrated into a standard cell culture well plate, with flexible cell compartment geometries and easy pipet access. Here, we present two new injection molded open microfluidic devices that also easily insert into standard cell culture well plates and standard culture workflows, allowing seamless adoption by biomedical researchers. These platforms allow culture and study of soluble factor communication among multiple cell types, and the microscale dimensions are well-suited for rare primary cells. Unique advances include optimized evaporation control within the well, manufacture with reproducible and cost-effective rapid injection molding, and compatibility with sample preparation workflows for high resolution microscopy (following well-established coverslip mounting procedures). In this work, we present several use cases that highlight the usability and widespread utility of our platform including culture of limited primary testis cells from surgical patients, microscopy readouts including immunocytochemistry and single molecule fluorescence in situ hybridization (smFISH), and coculture to study interactions between adipocytes and prostate cancer cells.

bioengineering↗

bioRxiv2018

Open channel droplet-based microfluidics

Droplet-based microfluidics enables compartmentalization and controlled manipulation of small volumes. Open microfluidics provides increased accessibility, adaptability, and ease of manufacturing compared to closed microfluidic platforms. Here, we begin to build a toolbox for the emerging field of open channel droplet-based microfluidics, combining the ease of use associated with open microfluidic platforms with the benefits of compartmentalization afforded by droplet-based microfluidics. We develop fundamental microfluidic features to control droplets flowing in an immiscible carrier fluid within open microfluidic systems. Our systems use capillary flow to move droplets and carrier fluid through open channels and are easily fabricated through 3D printing, micromilling, or injection molding; further, droplet generation can be accomplished by simply pipetting into the open channel. We demonstrate droplet incubation and transport for downstream experimentation and tunable droplet splitting in open channels driven by capillary flow. Potential applications of our toolbox for droplet manipulation in open channels include cell culture and analysis, on-chip microscale reactions, and reagent delivery.

bioengineering↗