The Experts below are selected from a list of 39 Experts worldwide ranked by ideXlab platform
Alex Fornito - One of the best experts on this subject based on the ideXlab platform.
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
NeuroImage, 2020Co-Authors: Kevin M. Aquino, Ben D. Fulcher, Linden Parkes, Kristina Sabaroedin, Alex FornitoAbstract:Abstract One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a Carpet Plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering Carpet Plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in Carpet Plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER .
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
bioRxiv, 2019Co-Authors: Kevin M. Aquino, Ben D. Fulcher, Linden Parkes, Kristina Sabaroedin, Alex FornitoAbstract:One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a Carpet Plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering Carpet Plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in Carpet Plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.
Brian Richard Mace - One of the best experts on this subject based on the ideXlab platform.
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Influence of Damping on the Bending and Twisting Modes of Flax Fibre-reinforced Polypropylene Composite
Fibers and Polymers, 2018Co-Authors: Md Zillur Rahman, Krishnan Jayaraman, Brian Richard MaceAbstract:The effects of damping on the bending and twisting modes of flax fibre-reinforced polypropylene composites are investigated. The laminate was manufactured by a vacuum bagging process; its dynamic behaviour was then found from the vibration measurements of a beam test specimen using an impulse hammer technique to frequencies of 1 kHz. The frequency response of a sample was measured, and the bending and twisting responses at resonance were used to estimate the natural frequency and loss factor. The single-degree-of-freedom circle-fit method and Newton’s divided differences formula were used to estimate the natural frequencies as well as the loss factors. The damping estimates were also investigated using a “Carpet” Plot. The results show significant variations in loss factors depending on the type of mode. The loss factor generally lies in the range of 1.7-2.2 % for the bending modes, while 4.8 % on average for the twisting modes. Numerical estimates of the response, and in particular the natural frequencies, were made using a Mechanical APDL (ANSYS parametric design language) finite element model, with the beam being discretised into a number of shell elements. The natural frequencies from the finite element analysis show reasonably good agreement (errors < 5 %) with the measured natural frequencies.
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Vibration damping of flax fibre-reinforced polypropylene composites
Fibers and Polymers, 2017Co-Authors: Md Zillur Rahman, Krishnan Jayaraman, Brian Richard MaceAbstract:This work investigates the effects of fibre content and fibre orientation on the damping of flax fibre-reinforced polypropylene composites. Laminates of various fibre contents were manufactured by a vacuum bagging process; their dynamic behaviour were then found from the vibration measurements of beam test specimens using an impulse hammer technique to frequencies of 1 kHz. The frequency response of a sample was measured and the response at resonance was used to estimate the natural frequency and loss factor. The single-degree-of-freedom circle-fit method and the Newton’s divided differences formula were used to estimate the natural frequencies as well as the loss factors. The damping estimates were also investigated using a “Carpet” Plot. Experiments were subsequently conducted on a range of samples with different fibre volume fractions and orientations. The results show significant variations in natural frequencies and loss factors according to the variations in fibre orientation. Composites containing 45°, 60° and 90° fibre orientation exhibit approximately the same natural frequencies. Composites with differing fibre orientations exhibit different loss factors for the various modes of vibration, and the maximum loss factor is obtained for the case of 45° fibre orientation, with the loss factor generally lying in the range of 2-7 %. It was found that the loss factor increases with increasing frequency and decreases slightly with increasing fibre content. These outcomes indicate that flax fibre-reinforced composite could be a commercially viable material for applications in which noise and vibration are significant issues and where a significant amount of damping is required.
Kevin M. Aquino - One of the best experts on this subject based on the ideXlab platform.
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
NeuroImage, 2020Co-Authors: Kevin M. Aquino, Ben D. Fulcher, Linden Parkes, Kristina Sabaroedin, Alex FornitoAbstract:Abstract One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a Carpet Plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering Carpet Plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in Carpet Plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER .
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
bioRxiv, 2019Co-Authors: Kevin M. Aquino, Ben D. Fulcher, Linden Parkes, Kristina Sabaroedin, Alex FornitoAbstract:One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a Carpet Plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering Carpet Plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in Carpet Plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.
Md Zillur Rahman - One of the best experts on this subject based on the ideXlab platform.
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Influence of Damping on the Bending and Twisting Modes of Flax Fibre-reinforced Polypropylene Composite
Fibers and Polymers, 2018Co-Authors: Md Zillur Rahman, Krishnan Jayaraman, Brian Richard MaceAbstract:The effects of damping on the bending and twisting modes of flax fibre-reinforced polypropylene composites are investigated. The laminate was manufactured by a vacuum bagging process; its dynamic behaviour was then found from the vibration measurements of a beam test specimen using an impulse hammer technique to frequencies of 1 kHz. The frequency response of a sample was measured, and the bending and twisting responses at resonance were used to estimate the natural frequency and loss factor. The single-degree-of-freedom circle-fit method and Newton’s divided differences formula were used to estimate the natural frequencies as well as the loss factors. The damping estimates were also investigated using a “Carpet” Plot. The results show significant variations in loss factors depending on the type of mode. The loss factor generally lies in the range of 1.7-2.2 % for the bending modes, while 4.8 % on average for the twisting modes. Numerical estimates of the response, and in particular the natural frequencies, were made using a Mechanical APDL (ANSYS parametric design language) finite element model, with the beam being discretised into a number of shell elements. The natural frequencies from the finite element analysis show reasonably good agreement (errors < 5 %) with the measured natural frequencies.
-
Vibration damping of flax fibre-reinforced polypropylene composites
Fibers and Polymers, 2017Co-Authors: Md Zillur Rahman, Krishnan Jayaraman, Brian Richard MaceAbstract:This work investigates the effects of fibre content and fibre orientation on the damping of flax fibre-reinforced polypropylene composites. Laminates of various fibre contents were manufactured by a vacuum bagging process; their dynamic behaviour were then found from the vibration measurements of beam test specimens using an impulse hammer technique to frequencies of 1 kHz. The frequency response of a sample was measured and the response at resonance was used to estimate the natural frequency and loss factor. The single-degree-of-freedom circle-fit method and the Newton’s divided differences formula were used to estimate the natural frequencies as well as the loss factors. The damping estimates were also investigated using a “Carpet” Plot. Experiments were subsequently conducted on a range of samples with different fibre volume fractions and orientations. The results show significant variations in natural frequencies and loss factors according to the variations in fibre orientation. Composites containing 45°, 60° and 90° fibre orientation exhibit approximately the same natural frequencies. Composites with differing fibre orientations exhibit different loss factors for the various modes of vibration, and the maximum loss factor is obtained for the case of 45° fibre orientation, with the loss factor generally lying in the range of 2-7 %. It was found that the loss factor increases with increasing frequency and decreases slightly with increasing fibre content. These outcomes indicate that flax fibre-reinforced composite could be a commercially viable material for applications in which noise and vibration are significant issues and where a significant amount of damping is required.
Ben D. Fulcher - One of the best experts on this subject based on the ideXlab platform.
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
NeuroImage, 2020Co-Authors: Kevin M. Aquino, Ben D. Fulcher, Linden Parkes, Kristina Sabaroedin, Alex FornitoAbstract:Abstract One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a Carpet Plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering Carpet Plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in Carpet Plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER .
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Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.
bioRxiv, 2019Co-Authors: Kevin M. Aquino, Ben D. Fulcher, Linden Parkes, Kristina Sabaroedin, Alex FornitoAbstract:One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a Carpet Plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering Carpet Plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in Carpet Plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.