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1. Mapping dysfunctional circuits in the frontal cortex using deep brain stimulation.

Author

Hollunder, Barbara, Ostrem, Jill, Sahin, Ilkem, Rajamani, Nanditha, Oxenford, Simón, Butenko, Konstantin, Neudorfer, Clemens, Reinhardt, Pablo, Zvarova, Patricia, Polosan, Mircea, Akram, Harith, Vissani, Matteo, Zhang, Chencheng, Sun, Bomin, Navratil, Pavel, Reich, Martin, Volkmann, Jens, Yeh, Fang-Cheng, Baldermann, Juan, Dembek, Till, Visser-Vandewalle, Veerle, Alho, Eduardo, Franceschini, Paulo, Nanda, Pranav, Finke, Carsten, Kühn, Andrea, Dougherty, Darin, Richardson, R, Bergman, Hagai, DeLong, Mahlon, Mazzoni, Alberto, Romito, Luigi, Tyagi, Himanshu, Zrinzo, Ludvic, Joyce, Eileen, Chabardes, Stephan, Li, Ningfei, Horn, Andreas, and Starr, Philip

Subjects
Humans, Deep Brain Stimulation, Brain, Motor Cortex, Parkinson Disease, Brain Mapping
Abstract

Frontal circuits play a critical role in motor, cognitive and affective processing, and their dysfunction may result in a variety of brain disorders. However, exactly which frontal domains mediate which (dys)functions remains largely elusive. We studied 534 deep brain stimulation electrodes implanted to treat four different brain disorders. By analyzing which connections were modulated for optimal therapeutic response across these disorders, we segregated the frontal cortex into circuits that had become dysfunctional in each of them. Dysfunctional circuits were topographically arranged from occipital to frontal, ranging from interconnections with sensorimotor cortices in dystonia, the primary motor cortex in Tourettes syndrome, the supplementary motor area in Parkinsons disease, to ventromedial prefrontal and anterior cingulate cortices in obsessive-compulsive disorder. Our findings highlight the integration of deep brain stimulation with brain connectomics as a powerful tool to explore couplings between brain structure and functional impairments in the human brain.

Published
2024

2. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Avesani, Paolo, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Chaumon, Maximilien, George, Nathalie, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Fischer, Jeremy, Faskowitz, Joshua, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuale, Hanson, Jamie L., Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., Bollmann, Steffen, Stewart, Ashley, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Published
2024
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3. Brain MRI Segmentation using Template-Based Training and Visual Perception Augmentation

Author

Yeh, Fang-Cheng

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

Deep learning models usually require sufficient training data to achieve high accuracy, but obtaining labeled data can be time-consuming and labor-intensive. Here we introduce a template-based training method to train a 3D U-Net model from scratch using only one population-averaged brain MRI template and its associated segmentation label. The process incorporated visual perception augmentation to enhance the model's robustness in handling diverse image inputs and mitigating overfitting. Leveraging this approach, we trained 3D U-Net models for mouse, rat, marmoset, rhesus, and human brain MRI to achieve segmentation tasks such as skull-stripping, brain segmentation, and tissue probability mapping. This tool effectively addresses the limited availability of training data and holds significant potential for expanding deep learning applications in image analysis, providing researchers with a unified solution to train deep neural networks with only one image sample.

Published
2023

4. brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Bollman, Steffen, Stewart, Ashley, Fischer, Jeremy, Faskowitz, Joshua, Chaumon, Maximilien, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuele, Hanson, Jamie L., Avesani, Paolo, Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., George, Nathalie, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Published
2023
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6. Author Correction: brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Avesani, Paolo, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Chaumon, Maximilien, George, Nathalie, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Fischer, Jeremy, Faskowitz, Joshua, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuale, Hanson, Jamie L., Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., Bollmann, Steffen, Stewart, Ashley, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Published
2024
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10. Elevated Systemic Inflammation Is Associated with Reduced Corticolimbic White Matter Integrity in Depression

Author

Thomas, MacGregor, Savitz, Jonathan, Zhang, Ye, Burrows, Kaiping, Smith, Ryan, Figueroa-Hall, Leandra, Kuplicki, Rayus, Khalsa, Sahib S, Taki, Yasuyuki, Teague, Tracy Kent, Irwin, Michael R, Yeh, Fang-Cheng, Paulus, Martin P, Zheng, Haixia, and Investigators, on behalf of Tulsa 1000

Subjects
Biochemistry and Cell Biology, Biological Sciences, Evolutionary Biology, Information and Computing Sciences, Applied Computing, Serious Mental Illness, Mental Health, Biomedical Imaging, Neurosciences, Brain Disorders, Depression, Major Depressive Disorder, Prevention, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, CRP, MDD, white matter integrity, quantitative anisotropy, connectometry analysis, diffusion MRI, Biochemistry and cell biology, Evolutionary biology, Applied computing
Abstract

(1) Background: Growing evidence indicates that inflammation can induce neural circuit dysfunction and plays a vital role in the pathogenesis of major depressive disorder (MDD). Nevertheless, whether inflammation affects the integrity of white matter pathways is only beginning to be explored. (2) Methods: We computed quantitative anisotropy (QA) from diffusion magnetic resonance imaging as an index of white matter integrity and regressed QA on C-reactive protein (CRP), controlling for age, sex, and BMI, in 176 participants with MDD. (3) Results: The QA values of several white matter tracts were negatively correlated with CRP concentration (standardized beta coefficient = -0.22, 95%CI = -0.38--0.06, FDR < 0.05). These tracts included the bilateral cortico-striatal tracts, thalamic radiations, inferior longitudinal fasciculi, corpus callosum (the forceps minor portion and the tapetum portion), cingulum bundles, and the left superior longitudinal fasciculus III. Importantly, the association remained robust after regressing up to twelve potential confounders. The bilateral fornix and a small portion of the thalamic radiation showed a positive association with CRP levels, but these associations did not remain significant after adjusting for confounders. (4) Conclusions: Peripheral inflammation may contribute to the etiology of MDD by impacting the microstructural integrity of brain corticolimbic white matter pathways.

Published
2022

11. 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

12. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Author

Cieslak, Matthew, Cook, Philip A, He, Xiaosong, Yeh, Fang-Cheng, Dhollander, Thijs, Adebimpe, Azeez, Aguirre, Geoffrey K, Bassett, Danielle S, Betzel, Richard F, Bourque, Josiane, Cabral, Laura M, Davatzikos, Christos, Detre, John A, Earl, Eric, Elliott, Mark A, Fadnavis, Shreyas, Fair, Damien A, Foran, Will, Fotiadis, Panagiotis, Garyfallidis, Eleftherios, Giesbrecht, Barry, Gur, Ruben C, Gur, Raquel E, Kelz, Max B, Keshavan, Anisha, Larsen, Bart S, Luna, Beatriz, Mackey, Allyson P, Milham, Michael P, Oathes, Desmond J, Perrone, Anders, Pines, Adam R, Roalf, David R, Richie-Halford, Adam, Rokem, Ariel, Sydnor, Valerie J, Tapera, Tinashe M, Tooley, Ursula A, Vettel, Jean M, Yeatman, Jason D, Grafton, Scott T, and Satterthwaite, Theodore D

Subjects
Biomedical Imaging, Bioengineering, Neurosciences, Brain, Diffusion Magnetic Resonance Imaging, Humans, Image Processing, Computer-Assisted, Programming Languages, Software, Workflow, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

Published
2021

13. Replicable association between human cytomegalovirus infection and reduced white matter fractional anisotropy in major depressive disorder

Author

Zheng, Haixia, Bergamino, Maurizio, Ford, Bart N, Kuplicki, Rayus, Yeh, Fang-Cheng, Bodurka, Jerzy, Burrows, Kaiping, Hunt, Peter W, Teague, T Kent, Irwin, Michael R, Yolken, Robert H, Paulus, Martin P, and Savitz, Jonathan

Subjects
Bioengineering, Serious Mental Illness, Brain Disorders, Clinical Research, Infectious Diseases, Depression, Mental Health, Major Depressive Disorder, Aetiology, 2.1 Biological and endogenous factors, Infection, Good Health and Well Being, Anisotropy, Cytomegalovirus Infections, Depressive Disorder, Major, Diffusion Tensor Imaging, Humans, White Matter, Tulsa 1000 Investigators, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Major depressive disorder (MDD) is associated with reductions in white matter microstructural integrity as measured by fractional anisotropy (FA), an index derived from diffusion tensor imaging (DTI). The neurotropic herpesvirus, human cytomegalovirus (HCMV), is a major cause of white matter pathology in immunosuppressed populations but its relationship with FA has never been tested in MDD despite the presence of inflammation and weakened antiviral immunity in a subset of depressed patients. We tested the relationship between FA and HCMV infection in two independent samples consisting of 176 individuals with MDD and 44 healthy controls (HC) (Discovery sample) and 88 participants with MDD and 48 HCs (Replication sample). Equal numbers of HCMV positive (HCMV+) and HCMV negative (HCMV-) groups within each sample were balanced on ten different clinical/demographic variables using propensity score matching. Anti-HCMV IgG antibodies were measured using a solid-phase ELISA. In the Discovery sample, significantly lower FA was observed in the right inferior fronto-occipital fasciculus (IFOF) in HCMV+ participants with MDD compared to HCMV- participants with MDD (cluster size 1316 mm3; pFWE

Published
2021

14. Establishing connectivity through microdissections of midbrain stimulation-related neural circuits

Author

Skandalakis, Georgios P, primary, Neudorfer, Clemens, additional, Payne, Caitlin A, additional, Bond, Evalina, additional, Tavakkoli, Armin D, additional, Barrios-Martinez, Jessica, additional, Trutti, Anne C, additional, Koutsarnakis, Christos, additional, Coenen, Volker A, additional, Komaitis, Spyridon, additional, Hadjipanayis, Constantinos G, additional, Stranjalis, George, additional, Yeh, Fang-Cheng, additional, Banihashemi, Layla, additional, Hong, Jennifer, additional, Lozano, Andres M, additional, Kogan, Michael, additional, Horn, Andreas, additional, Evans, Linton T, additional, and Kalyvas, Aristotelis, additional

Published
2024
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15. Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities.

Author

Hanalioglu, Sahin, Bahadir, Siyar, Isikay, Ilkay, Celtikci, Pinar, Celtikci, Emrah, Yeh, Fang-Cheng, Oguz, Kader, and Khaniyev, Taghi

Subjects
connectome, graph theory, hemispheric asymmetry, hub, hubness, network, parcellation, population
Abstract

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes. Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall hubness measure combining all five were studied. Group-level ranking-based aggregation method (measure-then-aggregate) was used to investigate network properties on population level. Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88-0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level. Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

Published
2021

22. Probabilistic coverage of the frontal aslant tract in young adults: Insights into individual variability, lateralization, and language functions

Author

Linn, Wen‐Jieh, primary, Barrios‐Martinez, Jessica, additional, Fernandes‐Cabral, David, additional, Jacquesson, Timothée, additional, Nuñez, Maximiliano, additional, Gomez, Ricardo, additional, Anania, Yury, additional, Fernandez‐Miranda, Juan, additional, and Yeh, Fang‐Cheng, additional

Published
2024
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25. Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Author

Maffei, Chiara, Girard, Gabriel, Schilling, Kurt G., Aydogan, Dogu Baran, Adluru, Nagesh, Zhylka, Andrey, Wu, Ye, Mancini, Matteo, Hamamci, Andac, Sarica, Alessia, Teillac, Achille, Baete, Steven H., Karimi, Davood, Yeh, Fang-Cheng, Yildiz, Mert E., Gholipour, Ali, Bihan-Poudec, Yann, Hiba, Bassem, Quattrone, Andrea, Quattrone, Aldo, Boshkovski, Tommy, Stikov, Nikola, Yap, Pew-Thian, de Luca, Alberto, Pluim, Josien, Leemans, Alexander, Prabhakaran, Vivek, Bendlin, Barbara B., Alexander, Andrew L., Landman, Bennett A., Canales-Rodríguez, Erick J., Barakovic, Muhamed, Rafael-Patino, Jonathan, Yu, Thomas, Rensonnet, Gaëtan, Schiavi, Simona, Daducci, Alessandro, Pizzolato, Marco, Fischi-Gomez, Elda, Thiran, Jean-Philippe, Dai, George, Grisot, Giorgia, Lazovski, Nikola, Puch, Santi, Ramos, Marc, Rodrigues, Paulo, Prčkovska, Vesna, Jones, Robert, Lehman, Julia, Haber, Suzanne N., and Yendiki, Anastasia

Published
2022
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28. Unveiling the axonal connectivity between the precuneus and temporal pole: Structural evidence from the cingulum pathways.

Author

Skandalakis, Georgios P., Linn, Wen‐Jieh, Yeh, Fang‐Cheng, Kazim, Syed Faraz, Komaitis, Spyridon, Neromyliotis, Eleftherios, Dimopoulos, Dimitrios, Drosos, Evangelos, Hadjipanayis, Constantinos G., Kongkham, Paul N., Zadeh, Gelareh, Stranjalis, George, Koutsarnakis, Christos, Kogan, Michael, Evans, Linton T., and Kalyvas, Aristotelis

Subjects
RHESUS monkeys, MICRODISSECTION, FORCEPS
Abstract

Neuroimaging studies have consistently demonstrated concurrent activation of the human precuneus and temporal pole (TP), both during resting‐state conditions and various higher‐order cognitive functions. However, the precise underlying structural connectivity between these brain regions remains uncertain despite significant advancements in neuroscience research. In this study, we investigated the connectivity of the precuneus and TP by employing parcellation‐based fiber micro‐dissections in human brains and fiber tractography techniques in a sample of 1065 human subjects and a sample of 41 rhesus macaques. Our results demonstrate the connectivity between the posterior precuneus area POS2 and the areas 35, 36, and TG of the TP via the fifth subcomponent of the cingulum (CB‐V) also known as parahippocampal cingulum. This finding contributes to our understanding of the connections within the posteromedial cortices, facilitating a more comprehensive integration of anatomy and function in both normal and pathological brain processes. Practitioner Points: Our investigation delves into the intricate architecture and connectivity patterns of subregions within the precuneus and temporal pole, filling a crucial gap in our knowledge.We revealed a direct axonal connection between the posterior precuneus (POS2) and specific areas (35, 35, and TG) of the temporal pole.The direct connections are part of the CB‐V pathway and exhibit a significant association with the cingulum, SRF, forceps major, and ILF.Population‐based human tractography and rhesus macaque fiber tractography showed consistent results that support micro‐dissection outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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31. 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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36. Increasing the Analytical Accessibility of Multishell and Diffusion Spectrum Imaging Data Using Generalized Q-Sampling Conversion

Author

Yeh, Fang-Cheng and Verstynen, Timothy D.

Subjects
Quantitative Biology - Neurons and Cognition, Physics - Medical Physics
Abstract

Many diffusion MRI researchers, including the Human Connectome Project (HCP), acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets are not readily accessible to high angular resolution diffusion imaging (HARDI) analysis methods that are popular in connectomics analysis. Here we introduce a scheme conversion approach that transforms multishell and DSI data into their corresponding HARDI representations, thereby empowering HARDI-based analytical methods to make use of data acquired using non-HARDI approaches. This method was evaluated on both phantom and in-vivo human data sets by acquiring multishell, DSI, and HARDI data simultaneously, and comparing the converted HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the phantom shows that the converted HARDI from DSI and multishell data strongly predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study shows that the converted HARDI can be reconstructed by constrained spherical deconvolution, and the fiber orientation distributions are consistent with those from the original HARDI. We further illustrate that our scheme conversion method can be applied to HCP data, and the converted HARDI do not appear to sacrifice angular resolution. Thus this novel approach can benefit all HARDI-based analysis approaches, allowing greater analytical accessibility to non-HARDI data, including data from the HCP.

Published
2014

40. The anatomy of the four streams of the prefrontal cortex. Preliminary evidence from a population based high definition tractography study

Author

Skandalakis, Georgios P., primary, Barrios-Martinez, Jessica, additional, Kazim, Syed Faraz, additional, Rumalla, Kavelin, additional, Courville, Evan N., additional, Mahto, Neil, additional, Kalyvas, Aristotelis, additional, Yeh, Fang-Cheng, additional, Hadjipanayis, Constantinos G., additional, Schmidt, Meic H., additional, and Kogan, Michael, additional

Published
2023
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41. Unveiling the Axonal Connectivity Between the Precuneus and Temporal Pole: Structural Evidence from the Cingulum Pathways

Author

Skandalakis, Georgios P., primary, Linn, Wen-Jieh, additional, Yeh, Fang-Cheng, additional, Kazim, Syed Faraz, additional, Komaitis, Spyridon, additional, Neromyliotis, Eleftherios, additional, Dimopoulos, Dimitrios, additional, Drossos, Evangelos, additional, Hadjipanayis, Constantinos G., additional, Kongkham, Paul N., additional, Zadeh, Gelareh, additional, Stranjalis, George, additional, Koutsarnakis, Christos, additional, Kogan, Michael, additional, and Kalyvas, Aristotelis, additional

Published
2023
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42. Mapping Topographic Structure in White Matter Pathways with Level Set Trees

Author

Kent, Brian P., Rinaldo, Alessandro, Yeh, Fang-Cheng, and Verstynen, Timothy

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees---which provide a concise representation of the hierarchical mode structure of probability density functions---offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N=30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber tracks and an efficient segmentation of the tracks that has empirical accuracy comparable to standard nonparametric clustering methods. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output.

Published
2013
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43. FuSSO: Functional Shrinkage and Selection Operator

Author

Oliva, Junier B., Poczos, Barnabas, Verstynen, Timothy, Singh, Aarti, Schneider, Jeff, Yeh, Fang-Cheng, and Tseng, Wen-Yih

Subjects
Statistics - Machine Learning, Computer Science - Learning, Mathematics - Statistics Theory
Abstract

We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.

Published
2013

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