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1. Sensitivity of quantitative diffusion MRI tractography and microstructure to anisotropic spatial sampling

Author

McMaster, Elyssa M., Newlin, Nancy R., Cho, Chloe, Rudravaram, Gaurav, Saunders, Adam M., Krishnan, Aravind R., Remedios, Lucas W., Kim, Michael E., Xu, Hanliang, Schilling, Kurt G., Rheault, François, Cutting, Laurie E., and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Purpose: Diffusion weighted MRI (dMRI) and its models of neural structure provide insight into human brain organization and variations in white matter. A recent study by McMaster, et al. showed that complex graph measures of the connectome, the graphical representation of a tractogram, vary with spatial sampling changes, but biases introduced by anisotropic voxels in the process have not been well characterized. This study uses microstructural measures (fractional anisotropy and mean diffusivity) and white matter bundle properties (bundle volume, length, and surface area) to further understand the effect of anisotropic voxels on microstructure and tractography. Methods: The statistical significance of the selected measures derived from dMRI data were assessed by comparing three white matter bundles at different spatial resolutions with 44 subjects from the Human Connectome Project Young Adult dataset scan/rescan data using the Wilcoxon Signed Rank test. The original isotropic resolution (1.25 mm isotropic) was explored with six anisotropic resolutions with 0.25 mm incremental steps in the z dimension. Then, all generated resolutions were upsampled to 1.25 mm isotropic and 1 mm isotropic. Results: There were statistically significant differences between at least one microstructural and one bundle measure at every resolution (p less than or equal to 0.05, corrected for multiple comparisons). Cohen's d coefficient evaluated the effect size of anisotropic voxels on microstructure and tractography. Conclusion: Fractional anisotropy and mean diffusivity cannot be recovered with basic up sampling from low quality data with gold standard data. However, the bundle measures from tractogram become more repeatable when voxels are resampled to 1 mm isotropic.

Published
2024

2. Harmonized connectome resampling for variance in voxel sizes

Author

McMaster, Elyssa M., Newlin, Nancy R., Rudravaram, Gaurav, Saunders, Adam M., Krishnan, Aravind R., Remedios, Lucas W., Kim, Michael E., Xu, Hanliang, Archer, Derek B., Schilling, Kurt G., Rheault, François, Cutting, Laurie E., and Landman, Bennett A.

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

To date, there has been no comprehensive study characterizing the effect of diffusion-weighted magnetic resonance imaging voxel resolution on the resulting connectome for high resolution subject data. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

Published
2024

3. BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation

Author

St-Onge, Etienne, Schilling, Kurt G, and Rheault, Francois

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This work presents BundleSeg, a reliable, reproducible, and fast method for extracting white matter pathways. The proposed method combines an iterative registration procedure with a recently developed precise streamline search algorithm that enables efficient segmentation of streamlines without the need for tractogram clustering or simplifying assumptions. We show that BundleSeg achieves improved repeatability and reproducibility than state-of-the-art segmentation methods, with significant speed improvements. The enhanced precision and reduced variability in extracting white matter connections offer a valuable tool for neuroinformatic studies, increasing the sensitivity and specificity of tractography-based studies of white matter pathways.

Published
2023

4. Robust Fiber Orientation Distribution Function Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI

Author

Yao, Tianyuan, Rheault, Francois, Cai, Leon Y, nath, Vishwesh, Asad, Zuhayr, Newlin, Nancy, Cui, Can, Deng, Ruining, Ramadass, Karthik, Shafer, Andrea, Resnick, Susan, Schilling, Kurt, Landman, Bennett A., and Huo, Yuankai

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multi-site and/or longitudinal diffusion studies. In this paper, we propose a novel data-driven deep constrained spherical deconvolution method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a new 3D volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The Baltimore Longitudinal Study of Aging (BLSA) dataset is employed for external validation. From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers., Comment: 33 pages, 7 figures

Published
2023

5. FIESTA: Autoencoders for accurate fiber segmentation in tractography

Author

Dumais, Félix, Legarreta, Jon Haitz, Lemaire, Carl, Poulin, Philippe, Rheault, François, Petit, Laurent, Barakovic, Muhamed, Magon, Stefano, Descoteaux, Maxime, and Jodoin, Pierre-Marc

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework., Comment: 36 pages, 13 figures, accepted in NeuroImage

Published
2022
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6. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author

Schilling, Kurt G, Rheault, François, Petit, Laurent, Hansen, Colin B, Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J, Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B, Vakharia, Vejay N, Duncan, John S, Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P, Korobova, Laura, Toga, Arthur W, Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J, Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal MW, Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D, Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E, Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J, Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A, Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J, Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S, Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N, Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A, Prabhakaran, Vivek, Alexander, Andrew L, Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, and Bayrak, Roza G

Subjects
Neurosciences, Biomedical Imaging, Algorithms, Diffusion Tensor Imaging, Dissection, Humans, Image Processing, Computer-Assisted, Neural Pathways, White Matter, Tractography, Bundle segmentation, White matter, Fiber pathways, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Published
2021

7. Filtering in tractography using autoencoders (FINTA)

Author

Legarreta, Jon Haitz, Petit, Laurent, Rheault, François, Theaud, Guillaume, Lemaire, Carl, Descoteaux, Maxime, and Jodoin, Pierre-Marc

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods, 62M20, 62M45, 68T01, 68U10, 92C55, I.5.1, J.3
Abstract

Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tractograms to train the autoencoder, and to learn a robust representation of brain streamlines. Such an embedding is then used to filter undesired streamline samples using a nearest neighbor algorithm. Our experiments on both synthetic and in vivo human brain diffusion MRI tractography data obtain accuracy scores exceeding the 90\% threshold on the test set. Results reveal that FINTA has a superior filtering performance compared to conventional, anatomy-based methods, and the RecoBundles state-of-the-art method. Additionally, we demonstrate that FINTA can be applied to partial tractograms without requiring changes to the framework. We also show that the proposed method generalizes well across different tracking methods and datasets, and shortens significantly the computation time for large (>1 M streamlines) tractograms. Together, this work brings forward a new deep learning framework in tractography based on autoencoders, which offers a flexible and powerful method for white matter filtering and bundling that could enhance tractometry and connectivity analyses., Comment: Preprint. Published in Medical Image Analysis 72 (2021) 102126

Published
2020
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10. Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Author

Girard, Gabriel, Rafael-Patiño, Jonathan, Truffet, Raphaël, Aydogan, Dogu Baran, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Bosticardo, Sara, Gabusi, Ilaria, Ocampo-Pineda, Mario, Battocchio, Matteo, Piskorova, Zuzana, Bontempi, Pietro, Schiavi, Simona, Daducci, Alessandro, Stafiej, Aleksandra, Ciupek, Dominika, Bogusz, Fabian, Pieciak, Tomasz, Frigo, Matteo, Sedlar, Sara, Deslauriers-Gauthier, Samuel, Kojčić, Ivana, Zucchelli, Mauro, Laghrissi, Hiba, Ji, Yang, Deriche, Rachid, Schilling, Kurt G, Landman, Bennett A., Cacciola, Alberto, Basile, Gianpaolo Antonio, Bertino, Salvatore, Newlin, Nancy, Kanakaraj, Praitayini, Rheault, Francois, Filipiak, Patryk, Shepherd, Timothy M., Lin, Ying-Chia, Placantonakis, Dimitris G., Boada, Fernando E., Baete, Steven H., Hernández-Gutiérrez, Erick, Ramírez-Manzanares, Alonso, Coronado-Leija, Ricardo, Stack-Sánchez, Pablo, Concha, Luis, Descoteaux, Maxime, Mansour L．, Sina, Seguin, Caio, Zalesky, Andrew, Marshall, Kenji, Canales-Rodríguez, Erick J., Wu, Ye, Ahmad, Sahar, Yap, Pew-Thian, Théberge, Antoine, Gagnon, Florence, Massi, Frédéric, Fischi-Gomez, Elda, Gardier, Rémy, Haro, Juan Luis Villarreal, Pizzolato, Marco, Caruyer, Emmanuel, and Thiran, Jean-Philippe

Published
2023
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13. Tractometry of the Human Connectome Project: resources and insights

Author

Kruper, John, primary, Hagen, McKenzie P., additional, Rheault, François, additional, Crane, Isaac, additional, Gilmore, Asa, additional, Narayan, Manjari, additional, Motwani, Keshav, additional, Lila, Eardi, additional, Rorden, Chris, additional, Yeatman, Jason D., additional, and Rokem, Ariel, additional

Published
2024
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15. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Author

Schilling, Kurt G., Rheault, François, Petit, Laurent, Hansen, Colin B., Nath, Vishwesh, Yeh, Fang-Cheng, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Fischi-Gomez, Elda, Pizzolato, Marco, Ocampo-Pineda, Mario, Schiavi, Simona, Canales-Rodríguez, Erick J., Daducci, Alessandro, Granziera, Cristina, Innocenti, Giorgio, Thiran, Jean-Philippe, Mancini, Laura, Wastling, Stephen, Cocozza, Sirio, Petracca, Maria, Pontillo, Giuseppe, Mancini, Matteo, Vos, Sjoerd B., Vakharia, Vejay N., Duncan, John S., Melero, Helena, Manzanedo, Lidia, Sanz-Morales, Emilio, Peña-Melián, Ángel, Calamante, Fernando, Attyé, Arnaud, Cabeen, Ryan P., Korobova, Laura, Toga, Arthur W., Vijayakumari, Anupa Ambili, Parker, Drew, Verma, Ragini, Radwan, Ahmed, Sunaert, Stefan, Emsell, Louise, De Luca, Alberto, Leemans, Alexander, Bajada, Claude J., Haroon, Hamied, Azadbakht, Hojjatollah, Chamberland, Maxime, Genc, Sila, Tax, Chantal M.W., Yeh, Ping-Hong, Srikanchana, Rujirutana, Mcknight, Colin D., Yang, Joseph Yuan-Mou, Chen, Jian, Kelly, Claire E., Yeh, Chun-Hung, Cochereau, Jerome, Maller, Jerome J., Welton, Thomas, Almairac, Fabien, Seunarine, Kiran K, Clark, Chris A., Zhang, Fan, Makris, Nikos, Golby, Alexandra, Rathi, Yogesh, O'Donnell, Lauren J., Xia, Yihao, Aydogan, Dogu Baran, Shi, Yonggang, Fernandes, Francisco Guerreiro, Raemaekers, Mathijs, Warrington, Shaun, Michielse, Stijn, Ramírez-Manzanares, Alonso, Concha, Luis, Aranda, Ramón, Meraz, Mariano Rivera, Lerma-Usabiaga, Garikoitz, Roitman, Lucas, Fekonja, Lucius S., Calarco, Navona, Joseph, Michael, Nakua, Hajer, Voineskos, Aristotle N., Karan, Philippe, Grenier, Gabrielle, Legarreta, Jon Haitz, Adluru, Nagesh, Nair, Veena A., Prabhakaran, Vivek, Alexander, Andrew L., Kamagata, Koji, Saito, Yuya, Uchida, Wataru, Andica, Christina, Abe, Masahiro, Bayrak, Roza G., Wheeler-Kingshott, Claudia A.M. Gandini, D'Angelo, Egidio, Palesi, Fulvia, Savini, Giovanni, Rolandi, Nicolò, Guevara, Pamela, Houenou, Josselin, López-López, Narciso, Mangin, Jean-François, Poupon, Cyril, Román, Claudio, Vázquez, Andrea, Maffei, Chiara, Arantes, Mavilde, Andrade, José Paulo, Silva, Susana Maria, Calhoun, Vince D., Caverzasi, Eduardo, Sacco, Simone, Lauricella, Michael, Pestilli, Franco, Bullock, Daniel, Zhan, Yang, Brignoni-Perez, Edith, Lebel, Catherine, Reynolds, Jess E, Nestrasil, Igor, Labounek, René, Lenglet, Christophe, Paulson, Amy, Aulicka, Stefania, Heilbronner, Sarah R., Heuer, Katja, Chandio, Bramsh Qamar, Guaje, Javier, Tang, Wei, Garyfallidis, Eleftherios, Raja, Rajikha, Anderson, Adam W., Landman, Bennett A., and Descoteaux, Maxime

Published
2021
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19. Progressive remodeling of structural networks following surgery for operculo-insular epilepsy.

Author

Obaid, Sami, Guberman, Guido I., St-Onge, Etienne, Campbell, Emma, Edde, Manon, Lamsam, Layton, Bouthillier, Alain, Weil, Alexander G., Daducci, Alessandro, Rheault, François, Nguyen, Dang K., and Descoteaux, Maxime

Subjects
EPILEPSY surgery, PARTIAL epilepsy, INSULAR cortex, EPILEPSY, SEIZURES (Medicine)
Abstract

Introduction: Operculo-insular epilepsy (OIE) is a rare condition amenable to surgery in well-selected cases. Despite the high rate of neurological complications associated with OIE surgery, most postoperative deficits recover fully and rapidly. We provide insights into this peculiar pattern of functional recovery by investigating the longitudinal reorganization of structural networks after surgery for OIE in 10 patients. Methods: Structural T1 and diffusion-weighted MRIs were performed before surgery (t0) and at 6  months (t1) and 12  months (t2) postoperatively. These images were processed with an original, comprehensive structural connectivity pipeline. Using our method, we performed comparisons between the t0 and t1 timepoints and between the t1 and t2 timepoints to characterize the progressive structural remodeling. Results: We found a widespread pattern of postoperative changes primarily in the surgical hemisphere, most of which consisted of reductions in connectivity strength (CS) and regional graph theoretic measures (rGTM) that reflect local connectivity. We also observed increases in CS and rGTMs predominantly in regions located near the resection cavity and in the contralateral healthy hemisphere. Finally, most structural changes arose in the first six months following surgery (i.e., between t0 and t1). Discussion: To our knowledge, this study provides the first description of postoperative structural connectivity changes following surgery for OIE. The ipsilateral reductions in connectivity unveiled by our analysis may result from the reversal of seizure-related structural alterations following postoperative seizure control. Moreover, the strengthening of connections in peri-resection areas and in the contralateral hemisphere may be compatible with compensatory structural plasticity, a process that could contribute to the recovery of functions seen following operculo-insular resections for focal epilepsy. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Bundle-Specific Tractography

Author

Rheault, Francois, St-Onge, Etienne, Sidhu, Jasmeen, Chenot, Quentin, Petit, Laurent, Descoteaux, Maxime, Hege, Hans-Christian, Series Editor, Hoffman, David, Series Editor, Johnson, Christopher R., Series Editor, Polthier, Konrad, Series Editor, Rumpf, Martin, Series Editor, Kaden, Enrico, editor, Grussu, Francesco, editor, Ning, Lipeng, editor, Tax, Chantal M. W., editor, and Veraart, Jelle, editor

Published
2018
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24. Edema-Informed Anatomically Constrained Particle Filter Tractography

Author

Deslauriers-Gauthier, Samuel, Parker, Drew, Rheault, François, Deriche, Rachid, Brem, Steven, Descoteaux, Maxime, Verma, Ragini, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Frangi, Alejandro F., editor, Schnabel, Julia A., editor, Davatzikos, Christos, editor, Alberola-López, Carlos, editor, and Fichtinger, Gabor, editor

Published
2018
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26. Limits to anatomical accuracy of diffusion tractography using modern approaches

Author

Schilling, Kurt G., Nath, Vishwesh, Hansen, Colin, Parvathaneni, Prasanna, Blaber, Justin, Gao, Yurui, Neher, Peter, Aydogan, Dogu Baran, Shi, Yonggang, Ocampo-Pineda, Mario, Schiavi, Simona, Daducci, Alessandro, Girard, Gabriel, Barakovic, Muhamed, Rafael-Patino, Jonathan, Romascano, David, Rensonnet, Gaëtan, Pizzolato, Marco, Bates, Alice, Fischi, Elda, Thiran, Jean-Philippe, Canales-Rodríguez, Erick J., Huang, Chao, Zhu, Hongtu, Zhong, Liming, Cabeen, Ryan, Toga, Arthur W., Rheault, Francois, Theaud, Guillaume, Houde, Jean-Christophe, Sidhu, Jasmeen, Chamberland, Maxime, Westin, Carl-Fredrik, Dyrby, Tim B., Verma, Ragini, Rathi, Yogesh, Irfanoglu, M. Okan, Thomas, Cibu, Pierpaoli, Carlo, Descoteaux, Maxime, Anderson, Adam W., and Landman, Bennett A.

Published
2019
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32. White Matter Microstructural Underpinnings of Mild Behavioral Impairment in Parkinson's Disease.

Author

Monchi, Oury, Pinilla‐Monsalve, Gabriel D., Almgren, Hannes, Ghahremani, Maryam, Kibreab, Mekale, Maarouf, Nadia, Kathol, Iris, Boré, Arnaud, Rheault, François, Descoteaux, Maxime, and Ismail, Zahinoor

Abstract

Background: Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early‐ to mid‐stage PD remain poorly understood. Objective: The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early‐ to mid‐stage PD using diffusion magnetic resonance imaging (dMRI). Methods: A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment‐Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel‐based apparent fiber density (AFD). Results: The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD‐MBI) to PD‐non‐MBI, as evidenced by increased RDt (η2 = 0.09, P = 0.004) and both decreased AFD (η2 = 0.05, P = 0.048) and FAt (η2 = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η2 = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η2 = 0.14, P = 0.008), and increased RDt (η2 = 0.25, P = 0.028) with decreased AFD (η2 = 0.10, P = 0.046) between the right OrG and the caudate nucleus. Conclusion: MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD‐MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Structural white matter properties and cognitive resilience to tau pathology.

Author

Qiu, Ting, Liu, Zhen‐Qi, Rheault, François, Legarreta, Jon Haitz, Valcourt Caron, Alex, St‐Onge, Frédéric, Strikwerda‐Brown, Cherie, Metz, Amelie, Dadar, Mahsa, Soucy, Jean‐Paul, Pichet Binette, Alexa, Spreng, R. Nathan, Descoteaux, Maxime, and Villeneuve, Sylvia

Abstract

INTRODUCTION: We assessed whether macro‐ and/or micro‐structural white matter properties are associated with cognitive resilience to Alzheimer's disease pathology years prior to clinical onset. METHODS: We examined whether global efficiency, an indicator of communication efficiency in brain networks, and diffusion measurements within the limbic network and default mode network moderate the association between amyloid‐β/tau pathology and cognitive decline. We also investigated whether demographic and health/risk factors are associated with white matter properties. RESULTS: Higher global efficiency of the limbic network, as well as free‐water corrected diffusion measures within the tracts of both networks, attenuated the impact of tau pathology on memory decline. Education, age, sex, white matter hyperintensities, and vascular risk factors were associated with white matter properties of both networks. DISCUSSION: White matter can influence cognitive resilience against tau pathology, and promoting education and vascular health may enhance optimal white matter properties. Highlights: Aβ and tau were associated with longitudinal memory change over ∼7.5 years.White matter properties attenuated the impact of tau pathology on memory change.Health/risk factors were associated with white matter properties. [ABSTRACT FROM AUTHOR]

Published
2024
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41. High‐frequency longitudinal white matter diffusion‐ and myelin‐based MRI database: Reliability and variability

Author

Edde, Manon, primary, Theaud, Guillaume, additional, Dumont, Matthieu, additional, Théberge, Antoine, additional, Valcourt‐Caron, Alex, additional, Gilbert, Guillaume, additional, Houde, Jean‐Christophe, additional, Maltais, Loika, additional, Rheault, François, additional, Spagnolo, Federico, additional, Barakovic, Muhamed, additional, Magon, Stefano, additional, and Descoteaux, Maxime, additional

Published
2023
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45. Author Correction: The challenge of mapping the human connectome based on diffusion tractography

Author

Maier-Hein, Klaus H., Neher, Peter F., Houde, Jean-Christophe, Côté, Marc-Alexandre, Garyfallidis, Eleftherios, Zhong, Jidan, Chamberland, Maxime, Yeh, Fang-Cheng, Lin, Ying-Chia, Ji, Qing, Reddick, Wilburn E., Glass, John O., Chen, David Qixiang, Feng, Yuanjing, Gao, Chengfeng, Wu, Ye, Ma, Jieyan, He, Renjie, Li, Qiang, Westin, Carl-Fredrik, Deslauriers-Gauthier, Samuel, González, J. Omar Ocegueda, Paquette, Michael, St-Jean, Samuel, Girard, Gabriel, Rheault, François, Sidhu, Jasmeen, Tax, Chantal M. W., Guo, Fenghua, Mesri, Hamed Y., Dávid, Szabolcs, Froeling, Martijn, Heemskerk, Anneriet M., Leemans, Alexander, Boré, Arnaud, Pinsard, Basile, Bedetti, Christophe, Desrosiers, Matthieu, Brambati, Simona, Doyon, Julien, Sarica, Alessia, Vasta, Roberta, Cerasa, Antonio, Quattrone, Aldo, Yeatman, Jason, Khan, Ali R., Hodges, Wes, Alexander, Simon, Romascano, David, Barakovic, Muhamed, Auría, Anna, Esteban, Oscar, Lemkaddem, Alia, Thiran, Jean-Philippe, Cetingul, H. Ertan, Odry, Benjamin L., Mailhe, Boris, Nadar, Mariappan S., Pizzagalli, Fabrizio, Prasad, Gautam, Villalon-Reina, Julio E., Galvis, Justin, Thompson, Paul M., De Santiago Requejo, Francisco, Laguna, Pedro Luque, Lacerda, Luis Miguel, Barrett, Rachel, Dell’Acqua, Flavio, Catani, Marco, Petit, Laurent, Caruyer, Emmanuel, Daducci, Alessandro, Dyrby, Tim B., Holland-Letz, Tim, Hilgetag, Claus C., Stieltjes, Bram, and Descoteaux, Maxime

Published
2019
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46. Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes.

Author

Newlin, Nancy R., Rheault, François, Schilling, Kurt G., and Landman, Bennett A.

Subjects
INVARIANT measures, DIFFUSION magnetic resonance imaging, INTRACLASS correlation
Abstract

Background: While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes. Purpose: The purpose was to define the relationship between streamline count and graph‐measure value, reproducibility, and repeatability. Study Type: Retrospective analysis of previously prospective study. Population: Ten healthy subjects, 70% female, aged 25.3 ± 5.9 years. Field Strength/Sequence: A 3‐T, T1‐weighted sequences and diffusion‐weighted imaging (DWI) with two gradient strengths (b‐values = 1200 and 3000 sec/mm2, echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2). Assessment: A total of 13 graph‐theory measures were derived for each subject by generating probabilistic whole‐brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived. Statistical Tests: Paired t‐test with P value <0.05 was used to compare graph‐measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility. Results: Modularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small‐worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided. Data Conclusion: Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count. Evidence Level: 1. Technical Efficacy: Stage 1. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

Author

Cai, Leon Y., primary, Lee, Ho Hin, additional, Newlin, Nancy R., additional, Kerley, Cailey I., additional, Kanakaraj, Praitayini, additional, Yang, Qi, additional, Johnson, Graham W., additional, Moyer, Daniel, additional, Schilling, Kurt G., additional, Rheault, François, additional, and Landman, Bennett A., additional

Published
2023
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49. High-frequency longitudinal white matter diffusion- & myelin-based MRI database: reliability and variability

Author

Edde, Manon, primary, Theaud, Guillaume, additional, Dumont, Matthieu, additional, Théberge, Antoine, additional, Valcourt-Caron, Alex, additional, Gilbert, Guillaume, additional, Houde, Jean-Christophe, additional, Maltais, Loika, additional, Rheault, François, additional, Spagnolo, Federico, additional, Barakovic, Muhamed, additional, Magon, Stefano, additional, and Descoteaux, Maxime, additional

Published
2022
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