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484 results on '"Glasser, Matthew F"'

1. An Accurate and Rapidly Calibrating Speech Neuroprosthesis

Author

Card, Nicholas S, Wairagkar, Maitreyee, Iacobacci, Carrina, Hou, Xianda, Singer-Clark, Tyler, Willett, Francis R, Kunz, Erin M, Fan, Chaofei, Vahdati Nia, Maryam, Deo, Darrel R, Srinivasan, Aparna, Choi, Eun Young, Glasser, Matthew F, Hochberg, Leigh R, Henderson, Jaimie M, Shahlaie, Kiarash, Stavisky, Sergey D, and Brandman, David M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Brain Disorders, Assistive Technology, Clinical Research, Neurosciences, Networking and Information Technology R&D (NITRD), ALS, Rehabilitation, Neurodegenerative, Rare Diseases, Neurological, Humans, Middle Aged, Male, Brain-Computer Interfaces, Amyotrophic Lateral Sclerosis, Dysarthria, Speech, Electrodes, Implanted, Calibration, Quadriplegia, Communication Aids for Disabled, Microelectrodes, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundBrain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.MethodsA 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.ResultsOn the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.ConclusionsIn a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

Published
2024

3. Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Author

Dahan, Simon, Xu, Hao, Williams, Logan Z. J., Fawaz, Abdulah, Yang, Chunhui, Coalson, Timothy S., Williams, Michelle C., Newby, David E., Edwards, A. David, Glasser, Matthew F., Young, Alistair A., Rueckert, Daniel, and Robinson, Emma C.

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

Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers, Comment: 10 pages, 3 figures, Submitted to IEEE Transactions on Medical Imaging

Published
2022

4. Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis

Author

Dahan, Simon, Fawaz, Abdulah, Williams, Logan Z. J., Yang, Chunhui, Coalson, Timothy S., Glasser, Matthew F., Edwards, A. David, Rueckert, Daniel, and Robinson, Emma C.

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

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold. Here, surface patching is achieved by representing spherical data as a sequence of triangular patches, extracted from a subdivided icosphere. A transformer model encodes the sequence of patches via successive multi-head self-attention layers while preserving the sequence resolution. We validate the performance of the proposed Surface Vision Transformer (SiT) on the task of phenotype regression from cortical surface metrics derived from the Developing Human Connectome Project (dHCP). Experiments show that the SiT generally outperforms surface CNNs, while performing comparably on registered and unregistered data. Analysis of transformer attention maps offers strong potential to characterise subtle cognitive developmental patterns., Comment: 22 pages, 6 figures, Accepted to MIDL 2022, OpenReview link https://openreview.net/forum?id=mpp843Bsf-

Published
2022

5. A high-performance speech neuroprosthesis

Author

Willett, Francis R., Kunz, Erin M., Fan, Chaofei, Avansino, Donald T., Wilson, Guy H., Choi, Eun Young, Kamdar, Foram, Glasser, Matthew F., Hochberg, Leigh R., Druckmann, Shaul, Shenoy, Krishna V., and Henderson, Jaimie M.

Published
2023
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6. ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

Author

Bass, Cher, da Silva, Mariana, Sudre, Carole, Williams, Logan Z. J., Tudosiu, Petru-Daniel, Alfaro-Almagro, Fidel, Fitzgibbon, Sean P., Glasser, Matthew F., Smith, Stephen M., and Robinson, Emma C.

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

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration to a global template, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN translation network called ICAM, to explicitly disentangle class relevant features from background confounds for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on Github https://github.com/CherBass/ICAM.

Published
2021

7. Minimal Specifications for Non-Human Primate MRI: Challenges in Standardizing and Harmonizing Data Collection

Author

Autio, Joonas A., Zhu, Qi, Li, Xiaolian, Glasser, Matthew F., Schwiedrzik, Caspar M., Fair, Damien A., Zimmermann, Jan, Yacoub, Essa, Menon, Ravi S., Van Essen, David C., Hayashi, Takuya, Russ, Brian, and Vanduffel, Wim

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and improve these efforts. However, consensus on minimal standards for data acquisition protocols and analysis pipelines for NHP imaging remains to be established, particularly for multi-center studies. Here, we draw parallels between NHP and human neuroimaging and provide minimal guidelines for harmonizing and standardizing data acquisition. We advocate robust translation of widely used open-access toolkits that are well established for analyzing human data. We also encourage the use of validated, automated pre-processing tools for analyzing NHP data sets. These guidelines aim to refine methodological and analytical strategies for small and large-scale NHP neuroimaging data. This will improve reproducibility of results, and accelerate the convergence between NHP and human neuroimaging strategies which will ultimately benefit fundamental and translational brain science.

Published
2020

8. The NonHuman Primate Neuroimaging & Neuroanatomy Project

Author

Hayashi, Takuya, Hou, Yujie, Glasser, Matthew F, Autio, Joonas A, Knoblauch, Kenneth, Inoue-Murayama, Miho, Coalson, Tim, Yacoub, Essa, Smith, Stephen, Kennedy, Henry, and Van Essen, David C

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Multi-modal neuroimaging projects are advancing our understanding of human brain architecture, function, connectivity using high-quality non-invasive data from many subjects. However, ground truth validation of connectivity using invasive tracers is not feasible in humans. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.

Published
2020

10. Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study

Author

Rashid, Barnaly, Glasser, Matthew F., Nichols, Thomas, Van Essen, David, Juttukonda, Meher R., Schwab, Nadine A., Greve, Douglas N, Yacoub, Essa, Lovely, Allison, Terpstra, Melissa, Harms, Michael P., Bookheimer, Susan Y., Ances, Beau M., Salat, David H., and Arnold, Steven E.

Published
2023
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14. Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation

Author

Williams, Logan Z. J., Fawaz, Abdulah, Glasser, Matthew F., Edwards, A. David, Robinson, Emma C., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abdulkadir, Ahmed, editor, Kia, Seyed Mostafa, editor, Habes, Mohamad, editor, Kumar, Vinod, editor, Rondina, Jane Maryam, editor, Tax, Chantal, editor, and Wolfers, Thomas, editor

Published
2021
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16. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects

Author

Harms, Michael P, Somerville, Leah H, Ances, Beau M, Andersson, Jesper, Barch, Deanna M, Bastiani, Matteo, Bookheimer, Susan Y, Brown, Timothy B, Buckner, Randy L, Burgess, Gregory C, Coalson, Timothy S, Chappell, Michael A, Dapretto, Mirella, Douaud, Gwenaëlle, Fischl, Bruce, Glasser, Matthew F, Greve, Douglas N, Hodge, Cynthia, Jamison, Keith W, Jbabdi, Saad, Kandala, Sridhar, Li, Xiufeng, Mair, Ross W, Mangia, Silvia, Marcus, Daniel, Mascali, Daniele, Moeller, Steen, Nichols, Thomas E, Robinson, Emma C, Salat, David H, Smith, Stephen M, Sotiropoulos, Stamatios N, Terpstra, Melissa, Thomas, Kathleen M, Tisdall, M Dylan, Ugurbil, Kamil, van der Kouwe, Andre, Woods, Roger P, Zöllei, Lilla, Van Essen, David C, and Yacoub, Essa

Subjects
Basic Behavioral and Social Science, Aging, Biomedical Imaging, Behavioral and Social Science, Neurosciences, Clinical Research, Adolescent, Adult, Aged, Aged, 80 and over, Brain, Child, Child, Preschool, Connectome, Female, Humans, Longevity, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Connectomics, Resting-state, Functional connectivity, Task, Diffusion, Perfusion, Development, Lifespan, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5-21), and HCP-A is enrolling 1200+ healthy adults (ages 36-100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22-35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

Published
2018

18. The Human Connectome Project: A retrospective

Author

Elam, Jennifer Stine, Glasser, Matthew F., Harms, Michael P., Sotiropoulos, Stamatios N., Andersson, Jesper L.R., Burgess, Gregory C., Curtiss, Sandra W., Oostenveld, Robert, Larson-Prior, Linda J., Schoffelen, Jan-Mathijs, Hodge, Michael R., Cler, Eileen A., Marcus, Daniel M., Barch, Deanna M., Yacoub, Essa, Smith, Stephen M., Ugurbil, Kamil, and Van Essen, David C.

Published
2021
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25. Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan

Author

Koike, Shinsuke, Tanaka, Saori C., Okada, Tomohisa, Aso, Toshihiko, Yamashita, Ayumu, Yamashita, Okito, Asano, Michiko, Maikusa, Norihide, Morita, Kentaro, Okada, Naohiro, Fukunaga, Masaki, Uematsu, Akiko, Togo, Hiroki, Miyazaki, Atsushi, Murata, Katsutoshi, Urushibata, Yuta, Autio, Joonas, Ose, Takayuki, Yoshimoto, Junichiro, Araki, Toshiyuki, Glasser, Matthew F., Van Essen, David C., Maruyama, Megumi, Sadato, Norihiro, Kawato, Mitsuo, Kasai, Kiyoto, Okamoto, Yasumasa, Hanakawa, Takashi, and Hayashi, Takuya

Published
2021
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37. An accurate and rapidly calibrating speech neuroprosthesis

Author

Card, Nicholas S, primary, Wairagkar, Maitreyee, additional, Iaccobacci, Carrina, additional, Hou, Xianda, additional, Singer-Clark, Tyler, additional, Willett, Francis R., additional, Kunz, Erin M., additional, Fan, Chaofei, additional, Vahdati Nia, Maryam, additional, Deo, Darrell R, additional, Choi, Eun Young, additional, Glasser, Matthew F, additional, Hochberg, Leigh R., additional, Henderson, Jaimie M, additional, Shahlaie, Kiarash, additional, Brandman, David M, additional, and Stavisky, Sergey D, additional

Published
2023
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41. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects

Author

Harms, Michael P., Somerville, Leah H., Ances, Beau M., Andersson, Jesper, Barch, Deanna M., Bastiani, Matteo, Bookheimer, Susan Y., Brown, Timothy B., Buckner, Randy L., Burgess, Gregory C., Coalson, Timothy S., Chappell, Michael A., Dapretto, Mirella, Douaud, Gwenaëlle, Fischl, Bruce, Glasser, Matthew F., Greve, Douglas N., Hodge, Cynthia, Jamison, Keith W., Jbabdi, Saad, Kandala, Sridhar, Li, Xiufeng, Mair, Ross W., Mangia, Silvia, Marcus, Daniel, Mascali, Daniele, Moeller, Steen, Nichols, Thomas E., Robinson, Emma C., Salat, David H., Smith, Stephen M., Sotiropoulos, Stamatios N., Terpstra, Melissa, Thomas, Kathleen M., Tisdall, M. Dylan, Ugurbil, Kamil, van der Kouwe, Andre, Woods, Roger P., Zöllei, Lilla, Van Essen, David C., and Yacoub, Essa

Published
2018
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44. Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

Author

Bozek, Jelena, Makropoulos, Antonios, Schuh, Andreas, Fitzgibbon, Sean, Wright, Robert, Glasser, Matthew F., Coalson, Timothy S., O'Muircheartaigh, Jonathan, Hutter, Jana, Price, Anthony N., Cordero-Grande, Lucilio, Teixeira, Rui Pedro A.G., Hughes, Emer, Tusor, Nora, Baruteau, Kelly Pegoretti, Rutherford, Mary A., Edwards, A. David, Hajnal, Joseph V., Smith, Stephen M., Rueckert, Daniel, Jenkinson, Mark, and Robinson, Emma C.

Published
2018
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46. Multimodal surface matching with higher-order smoothness constraints

Author

Robinson, Emma C., Garcia, Kara, Glasser, Matthew F., Chen, Zhengdao, Coalson, Timothy S., Makropoulos, Antonios, Bozek, Jelena, Wright, Robert, Schuh, Andreas, Webster, Matthew, Hutter, Jana, Price, Anthony, Cordero Grande, Lucilio, Hughes, Emer, Tusor, Nora, Bayly, Philip V., Van Essen, David C., Smith, Stephen M., Edwards, A. David, Hajnal, Joseph, Jenkinson, Mark, Glocker, Ben, and Rueckert, Daniel

Published
2018
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50. Hand classification of fMRI ICA noise components

Author

Griffanti, Ludovica, Douaud, Gwenaëlle, Bijsterbosch, Janine, Evangelisti, Stefania, Alfaro-Almagro, Fidel, Glasser, Matthew F., Duff, Eugene P., Fitzgibbon, Sean, Westphal, Robert, Carone, Davide, Beckmann, Christian F., and Smith, Stephen M.

Published
2017
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