The Experts below are selected from a list of 33756 Experts worldwide ranked by ideXlab platform
Avniel Singh Ghuman - One of the best experts on this subject based on the ideXlab platform.
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multi Connection Pattern analysis decoding the representational content of neural communication
NeuroImage, 2017Co-Authors: Avniel Singh Ghuman, Yuanning Li, Robert M RichardsonAbstract:Abstract The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity Patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions.
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Multi-Connection Pattern Analysis: Decoding the Representational Content of Neural Communication
2016Co-Authors: Avniel Singh GhumanAbstract:What information is represented in the interactions between neural populations is unknown due to the lack of multivariate methods for decoding the representational content of neural communication. Here we present Multi-Connection Pattern Analysis (MCPA), which probes the involvement of distributed computational processing and probe the representational structure of neural interactions. MCPA learns mappings between the activity Patterns as a factor of the information being processed. These maps are used to predict the multivariate activity Pattern from one neural population based on the activity Pattern from another population. Simulations demonstrate the efficacy of MCPA in realistic circumstances. Applying MCPA to fMRI data shows that interactions between visual cortex regions are sensitive to information that distinguishes individual natural images. These results suggest that image individuation occurs through interactive computation across the visual processing network. Thus, MCPA can be used to assess the information processed in the coupled activity of interacting neural circuits.
Michael Stubert Berger - One of the best experts on this subject based on the ideXlab platform.
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out of sequence preventative cell dispatching for multicast input queued space memory memory clos network
High Performance Switching and Routing, 2011Co-Authors: Sarah Ruepp, Michael Stubert BergerAbstract:This paper proposes two out-of-sequence (OOS) preventative cell dispatching algorithms for the multicast input-queued space-memory-memory (IQ-SMM) Clos-network switch architecture, i.e. the multicast flow-based DSRR (MF-DSRR) and the multicast flow-based round-robin (MFRR). Treating each cell independently, the desynchronized static round-robin (DSRR) cell dispatching scheme can evenly distribute cells to the central switching modules, however, its frequent change of the input switching module Connection Pattern causes a serious OOS problem to the IQ-SMM architecture. Therefore large reassembly buffers are required at the output ports and high reassembly delay can degrade the multicast services. MF-DSRR can reduce the OOS problem and leverages the principle of DSRR to obtain a low complexity, however it fails to eliminate the in-packet OOS where cells of the same packet are disordered. Using more resources compared to MF-DSRR, MFRR is able to eliminate the in-packet OOS, resulting in a significant reduction of reassembly buffer size and delay.
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out of sequence prevention for multicast input queuing space memory memory clos network
IEEE Communications Letters, 2011Co-Authors: Sarah Ruepp, Michael Stubert BergerAbstract:This paper proposes two cell dispatching algorithms for the input-queuing space-memory-memory (IQ-SMM) Clos-network to reduce out-of-sequence (OOS) for multicast traffic. The frequent Connection Pattern change of DSRR results in a severe OOS problem. Based on the principle of DSRR, MF-DSRR is able to reduce OOS but still suffers from it under high traffic load. MFRR maintains the Connection Pattern separately for each input and can eliminate the in-packet OOS and thus significantly reduces the reassembly buffer size and delay.
Tuo Zhang - One of the best experts on this subject based on the ideXlab platform.
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joint representation of connectome scale structural and functional profiles for identification of consistent cortical landmarks in macaque brain
Brain Imaging and Behavior, 2019Co-Authors: Shu Zhang, Xi Jiang, Tuo Zhang, Wei Zhang, Hanbo Chen, Yu Zhao, Jinglei Lv, Brittany R HowellAbstract:Discovery and representation of common structural and functional cortical architectures has been a significant yet challenging problem for years. Due to the remarkable variability of structural and functional cortical architectures in human brain, it is challenging to jointly represent a common cortical architecture which can comprehensively encode both structure and function characteristics. In order to better understand this challenge and considering that macaque monkey brain has much less variability in structure and function compared with human brain, in this paper, we propose a novel computational framework to apply our DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) and HAFNI (Holistic Atlases of Functional Networks and Interactions) frameworks on macaque brains, in order to jointly represent structural and functional connectome-scale profiles for identification of a set of consistent and common cortical landmarks across different macaque brains based on multimodal DTI and resting state fMRI (rsfMRI) data. Experimental results demonstrate that 100 consistent and common cortical landmarks are successfully identified via the proposed framework, each of which has reasonably accurate anatomical, structural fiber Connection Pattern, and functional correspondences across different macaque brains. This set of 100 landmarks offer novel insights into the structural and functional cortical architectures in macaque brains.
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MICCAI (1) - Fiber Connection Pattern-Guided Structured Sparse Representation of Whole-Brain fMRI Signals for Functional Network Inference
Lecture Notes in Computer Science, 2015Co-Authors: Xi Jiang, Tuo Zhang, Qinghua Zhao, Lei Guo, Tianming LiuAbstract:A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging fMRI signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber Connection Pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber Connection Pattern-guided structured sparse representation of whole-brain resting state fMRI rsfMRI signals to infer functional networks. In particular, the fiber Connection Patterns derived from diffusion tensor imaging DTI data are adopted as the Connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber Connection Patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project HCP rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial Pattern correspondence and fiber Connection Pattern consistency across individual subjects.
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fiber Connection Pattern guided structured sparse representation of whole brain fmri signals for functional network inference
Medical Image Computing and Computer-Assisted Intervention, 2015Co-Authors: Xi Jiang, Tuo Zhang, Qinghua Zhao, Lei Guo, Tianming LiuAbstract:A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging fMRI signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber Connection Pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber Connection Pattern-guided structured sparse representation of whole-brain resting state fMRI rsfMRI signals to infer functional networks. In particular, the fiber Connection Patterns derived from diffusion tensor imaging DTI data are adopted as the Connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber Connection Patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project HCP rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial Pattern correspondence and fiber Connection Pattern consistency across individual subjects.
Xi Jiang - One of the best experts on this subject based on the ideXlab platform.
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joint representation of connectome scale structural and functional profiles for identification of consistent cortical landmarks in macaque brain
Brain Imaging and Behavior, 2019Co-Authors: Shu Zhang, Xi Jiang, Tuo Zhang, Wei Zhang, Hanbo Chen, Yu Zhao, Jinglei Lv, Brittany R HowellAbstract:Discovery and representation of common structural and functional cortical architectures has been a significant yet challenging problem for years. Due to the remarkable variability of structural and functional cortical architectures in human brain, it is challenging to jointly represent a common cortical architecture which can comprehensively encode both structure and function characteristics. In order to better understand this challenge and considering that macaque monkey brain has much less variability in structure and function compared with human brain, in this paper, we propose a novel computational framework to apply our DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) and HAFNI (Holistic Atlases of Functional Networks and Interactions) frameworks on macaque brains, in order to jointly represent structural and functional connectome-scale profiles for identification of a set of consistent and common cortical landmarks across different macaque brains based on multimodal DTI and resting state fMRI (rsfMRI) data. Experimental results demonstrate that 100 consistent and common cortical landmarks are successfully identified via the proposed framework, each of which has reasonably accurate anatomical, structural fiber Connection Pattern, and functional correspondences across different macaque brains. This set of 100 landmarks offer novel insights into the structural and functional cortical architectures in macaque brains.
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MICCAI (1) - Fiber Connection Pattern-Guided Structured Sparse Representation of Whole-Brain fMRI Signals for Functional Network Inference
Lecture Notes in Computer Science, 2015Co-Authors: Xi Jiang, Tuo Zhang, Qinghua Zhao, Lei Guo, Tianming LiuAbstract:A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging fMRI signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber Connection Pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber Connection Pattern-guided structured sparse representation of whole-brain resting state fMRI rsfMRI signals to infer functional networks. In particular, the fiber Connection Patterns derived from diffusion tensor imaging DTI data are adopted as the Connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber Connection Patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project HCP rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial Pattern correspondence and fiber Connection Pattern consistency across individual subjects.
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fiber Connection Pattern guided structured sparse representation of whole brain fmri signals for functional network inference
Medical Image Computing and Computer-Assisted Intervention, 2015Co-Authors: Xi Jiang, Tuo Zhang, Qinghua Zhao, Lei Guo, Tianming LiuAbstract:A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging fMRI signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber Connection Pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber Connection Pattern-guided structured sparse representation of whole-brain resting state fMRI rsfMRI signals to infer functional networks. In particular, the fiber Connection Patterns derived from diffusion tensor imaging DTI data are adopted as the Connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber Connection Patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project HCP rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial Pattern correspondence and fiber Connection Pattern consistency across individual subjects.
Tianming Liu - One of the best experts on this subject based on the ideXlab platform.
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MICCAI (1) - Fiber Connection Pattern-Guided Structured Sparse Representation of Whole-Brain fMRI Signals for Functional Network Inference
Lecture Notes in Computer Science, 2015Co-Authors: Xi Jiang, Tuo Zhang, Qinghua Zhao, Lei Guo, Tianming LiuAbstract:A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging fMRI signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber Connection Pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber Connection Pattern-guided structured sparse representation of whole-brain resting state fMRI rsfMRI signals to infer functional networks. In particular, the fiber Connection Patterns derived from diffusion tensor imaging DTI data are adopted as the Connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber Connection Patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project HCP rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial Pattern correspondence and fiber Connection Pattern consistency across individual subjects.
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fiber Connection Pattern guided structured sparse representation of whole brain fmri signals for functional network inference
Medical Image Computing and Computer-Assisted Intervention, 2015Co-Authors: Xi Jiang, Tuo Zhang, Qinghua Zhao, Lei Guo, Tianming LiuAbstract:A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance imaging fMRI signals. However, previous approaches are pure data-driven and do not integrate brain science domain knowledge when reconstructing functional networks. The group-wise correspondence of the reconstructed functional networks across individual subjects is thus not well guaranteed. Moreover, the fiber Connection Pattern consistency of those functional networks across subjects is largely unknown. To tackle these challenges, in this paper, we propose a novel fiber Connection Pattern-guided structured sparse representation of whole-brain resting state fMRI rsfMRI signals to infer functional networks. In particular, the fiber Connection Patterns derived from diffusion tensor imaging DTI data are adopted as the Connectional features to perform consistent cortical parcellation across subjects. Those consistent parcellated regions with similar fiber Connection Patterns are then employed as the group structured constraint to guide group-wise multi-task sparse representation of whole-brain rsfMRI signals to reconstruct functional networks. Using the recently publicly released high quality Human Connectome Project HCP rsfMRI and DTI data, our experimental results demonstrate that the identified functional networks via the proposed approach have both reasonable spatial Pattern correspondence and fiber Connection Pattern consistency across individual subjects.