The Experts below are selected from a list of 14220 Experts worldwide ranked by ideXlab platform
Vince Grolmusz - One of the best experts on this subject based on the ideXlab platform.
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Mapping correlations of psychological and structural Connectome properties of the dataset of the human Connectome project with the maximum spanning tree method
Brain Imaging and Behavior, 2019Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Genome-wide association studies (GWAS) opened new horizons in genomics and medicine by discovering novel genetic factors in numerous health conditions. The analogous analysis of the correlations of large quantities of psychological and brain imaging measures may yield similarly striking results in the brain science. Smith et al. (Nat Neurosci. 18(11): 1565–1567, 2015 ) presented a study of the associations between MRI-detected resting-state functional Connectomes and behavioral data, based on the Human Connectome Project’s (HCP) data release. Here we analyze the pairwise correlations between 717 psychological-, anatomical- and structural Connectome–properties, based also on the Human Connectome Project’s 500-subject dataset. For the Connectome properties, we have focused on the structural (or anatomical) Connectomes, instead of the functional Connectomes. For the structural Connectome analysis we have computed and publicly deposited structural braingraphs at the site http://braingraph.org . Numerous non-trivial and hard-to-compute graph-theoretical parameters (like minimum bisection width, minimum vertex cover, eigenvalue gap, maximum matching number, maximum fractional matching number) were computed for braingraphs of each subject, gained from the left- and right hemispheres and the whole brain. The correlations of these parameters, as well as other anatomical and behavioral measures were detected and analyzed. For discovering and visualizing the most interesting correlations in the 717 x 717 matrix, we have applied the maximum spanning tree method. Apart from numerous natural correlations, which describe parameters computable or approximable from one another, we have found several significant, novel correlations in the dataset, e.g., between the score of the NIH Toolbox 9-hole Pegboard Dexterity Test and the maximum weight graph theoretical matching in the left hemisphere. We also have found correlations described very recently and independently from the HCP-dataset: e.g., between gambling behavior and the number of the connections leaving the insula: these already known findings independently validate the power of our method.
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The frequent subgraphs of the Connectome of the human brain.
Cognitive neurodynamics, 2019Co-Authors: Máté Fellner, Bálint Varga, Vince GrolmuszAbstract:In mapping the human structural Connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (1) discovery that women’s Connectomes have deeper and richer connectivity-related graph parameters like those of men, or (2) the description of more and less conservatively connected lobes and cerebral regions, and (3) the discovery of the phenomenon of the consensus Connectome dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and function. The present contribution describes the frequent connected subgraphs of at most six edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected subgraphs that are more frequent in female or male Connectomes. While there is no difference in the number of k edge connected subgraphs in males or females for $${\text{k}} = 1$$ , and for $${\text{k}} = 2$$ males have slightly more frequent subgraphs, for $${\text{k}} = 6$$ there is a very strong advantage in the case of female braingraphs. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.
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High-resolution directed human Connectomes and the Consensus Connectome Dynamics.
PloS one, 2019Co-Authors: Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince GrolmuszAbstract:Here we show a method of directing the edges of the Connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional Connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of Connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the “Consensus Connectome Dynamics”, described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed Connectomes publicly available at the site http://braingraph.org. We also show the robustness of our edge directing method in four independently chosen Connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human Connectome.
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Comparing advanced graph-theoretical parameters of the Connectomes of the lobes of the human brain
Cognitive Neurodynamics, 2018Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or Connectomes which were defined in the last twenty years for enormous networks—like the graph of the World Wide Web—with hundreds of millions of nodes. Since the Connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the Connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women’s Connectomes than in men’s. In the present contribution we compare numerous graph parameters in the three largest lobes—frontal, parietal, temporal—and in both hemispheres of the human brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are held for the left frontal versus the right frontal and the right temporal versus the left temporal lobes.
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The Frequent Subgraphs of the Connectome of the Human Brain
arXiv: Neurons and Cognition, 2017Co-Authors: Máté Fellner, Bálint Varga, Vince GrolmuszAbstract:In mapping the human structural Connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (i) discovery that women's Connectomes have deeper and richer connectivity-related graph parameters like those of men, or (ii) the description of more and less conservatively connected lobes and cerebral regions, and (iii) the discovery of the phenomenon of the Consensus Connectome Dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and functions. The present contribution describes the frequent connected subgraphs (instead of sub-circuits) of at most 6 edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected sub-graphs that are more frequent in female or the male Connectome. While our results describe subgraphs, instead of sub-circuits, we need to note that all macroscopic sub-circuits correspond to an underlying connected subgraph. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.
Balázs Szalkai - One of the best experts on this subject based on the ideXlab platform.
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Mapping correlations of psychological and structural Connectome properties of the dataset of the human Connectome project with the maximum spanning tree method
Brain Imaging and Behavior, 2019Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Genome-wide association studies (GWAS) opened new horizons in genomics and medicine by discovering novel genetic factors in numerous health conditions. The analogous analysis of the correlations of large quantities of psychological and brain imaging measures may yield similarly striking results in the brain science. Smith et al. (Nat Neurosci. 18(11): 1565–1567, 2015 ) presented a study of the associations between MRI-detected resting-state functional Connectomes and behavioral data, based on the Human Connectome Project’s (HCP) data release. Here we analyze the pairwise correlations between 717 psychological-, anatomical- and structural Connectome–properties, based also on the Human Connectome Project’s 500-subject dataset. For the Connectome properties, we have focused on the structural (or anatomical) Connectomes, instead of the functional Connectomes. For the structural Connectome analysis we have computed and publicly deposited structural braingraphs at the site http://braingraph.org . Numerous non-trivial and hard-to-compute graph-theoretical parameters (like minimum bisection width, minimum vertex cover, eigenvalue gap, maximum matching number, maximum fractional matching number) were computed for braingraphs of each subject, gained from the left- and right hemispheres and the whole brain. The correlations of these parameters, as well as other anatomical and behavioral measures were detected and analyzed. For discovering and visualizing the most interesting correlations in the 717 x 717 matrix, we have applied the maximum spanning tree method. Apart from numerous natural correlations, which describe parameters computable or approximable from one another, we have found several significant, novel correlations in the dataset, e.g., between the score of the NIH Toolbox 9-hole Pegboard Dexterity Test and the maximum weight graph theoretical matching in the left hemisphere. We also have found correlations described very recently and independently from the HCP-dataset: e.g., between gambling behavior and the number of the connections leaving the insula: these already known findings independently validate the power of our method.
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High-resolution directed human Connectomes and the Consensus Connectome Dynamics.
PloS one, 2019Co-Authors: Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince GrolmuszAbstract:Here we show a method of directing the edges of the Connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional Connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of Connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the “Consensus Connectome Dynamics”, described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed Connectomes publicly available at the site http://braingraph.org. We also show the robustness of our edge directing method in four independently chosen Connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human Connectome.
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Comparing advanced graph-theoretical parameters of the Connectomes of the lobes of the human brain
Cognitive Neurodynamics, 2018Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or Connectomes which were defined in the last twenty years for enormous networks—like the graph of the World Wide Web—with hundreds of millions of nodes. Since the Connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the Connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women’s Connectomes than in men’s. In the present contribution we compare numerous graph parameters in the three largest lobes—frontal, parietal, temporal—and in both hemispheres of the human brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are held for the left frontal versus the right frontal and the right temporal versus the left temporal lobes.
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Comparing Advanced Graph-Theoretical Parameters of the Connectomes of the Lobes of the Human Brain
arXiv: Neurons and Cognition, 2017Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or Connectomes which were defined in the last twenty years for enormous networks -- like the graph of the World Wide Web -- with hundreds of millions of nodes. Since the Connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the Connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women's Connectomes than in men's. In the present contribution we compare numerous graph parameters in the three largest lobes --- frontal, parietal, temporal --- and in both hemispheres of the brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are hold for the left frontal vs. the right frontal and for the right temporal vs. the left temporal lobes.
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High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics
arXiv: Neurons and Cognition, 2016Co-Authors: Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince GrolmuszAbstract:Here we show a method of directing the edges of the Connectomes, prepared from diffusion tensor imaging (DTI) datasets from the human brain. Before the present work, no high-definition directed braingraphs (or Connectomes) were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional Connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of Connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the "Consensus Connectome Dynamics" (CCD), described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed Connectomes publicly available at the site \url{this http URL}. We also show the robustness of our edge directing method in four independently chosen Connectome datasets: we have found that 86\% of the edges, which were present in all four datasets, get the very same directions in all datasets; therefore the direction method is robust, it does not depend on the particular choice of the dataset. We think that our present contribution opens up new possibilities in the analysis of the high-definition human Connectome: from now on we can work with a robust assignment of directions of the connections of the human brain.
Bálint Varga - One of the best experts on this subject based on the ideXlab platform.
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Mapping correlations of psychological and structural Connectome properties of the dataset of the human Connectome project with the maximum spanning tree method
Brain Imaging and Behavior, 2019Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Genome-wide association studies (GWAS) opened new horizons in genomics and medicine by discovering novel genetic factors in numerous health conditions. The analogous analysis of the correlations of large quantities of psychological and brain imaging measures may yield similarly striking results in the brain science. Smith et al. (Nat Neurosci. 18(11): 1565–1567, 2015 ) presented a study of the associations between MRI-detected resting-state functional Connectomes and behavioral data, based on the Human Connectome Project’s (HCP) data release. Here we analyze the pairwise correlations between 717 psychological-, anatomical- and structural Connectome–properties, based also on the Human Connectome Project’s 500-subject dataset. For the Connectome properties, we have focused on the structural (or anatomical) Connectomes, instead of the functional Connectomes. For the structural Connectome analysis we have computed and publicly deposited structural braingraphs at the site http://braingraph.org . Numerous non-trivial and hard-to-compute graph-theoretical parameters (like minimum bisection width, minimum vertex cover, eigenvalue gap, maximum matching number, maximum fractional matching number) were computed for braingraphs of each subject, gained from the left- and right hemispheres and the whole brain. The correlations of these parameters, as well as other anatomical and behavioral measures were detected and analyzed. For discovering and visualizing the most interesting correlations in the 717 x 717 matrix, we have applied the maximum spanning tree method. Apart from numerous natural correlations, which describe parameters computable or approximable from one another, we have found several significant, novel correlations in the dataset, e.g., between the score of the NIH Toolbox 9-hole Pegboard Dexterity Test and the maximum weight graph theoretical matching in the left hemisphere. We also have found correlations described very recently and independently from the HCP-dataset: e.g., between gambling behavior and the number of the connections leaving the insula: these already known findings independently validate the power of our method.
-
The frequent subgraphs of the Connectome of the human brain.
Cognitive neurodynamics, 2019Co-Authors: Máté Fellner, Bálint Varga, Vince GrolmuszAbstract:In mapping the human structural Connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (1) discovery that women’s Connectomes have deeper and richer connectivity-related graph parameters like those of men, or (2) the description of more and less conservatively connected lobes and cerebral regions, and (3) the discovery of the phenomenon of the consensus Connectome dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and function. The present contribution describes the frequent connected subgraphs of at most six edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected subgraphs that are more frequent in female or male Connectomes. While there is no difference in the number of k edge connected subgraphs in males or females for $${\text{k}} = 1$$ , and for $${\text{k}} = 2$$ males have slightly more frequent subgraphs, for $${\text{k}} = 6$$ there is a very strong advantage in the case of female braingraphs. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.
-
High-resolution directed human Connectomes and the Consensus Connectome Dynamics.
PloS one, 2019Co-Authors: Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince GrolmuszAbstract:Here we show a method of directing the edges of the Connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional Connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of Connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the “Consensus Connectome Dynamics”, described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed Connectomes publicly available at the site http://braingraph.org. We also show the robustness of our edge directing method in four independently chosen Connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human Connectome.
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Comparing advanced graph-theoretical parameters of the Connectomes of the lobes of the human brain
Cognitive Neurodynamics, 2018Co-Authors: Balázs Szalkai, Bálint Varga, Vince GrolmuszAbstract:Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or Connectomes which were defined in the last twenty years for enormous networks—like the graph of the World Wide Web—with hundreds of millions of nodes. Since the Connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the Connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women’s Connectomes than in men’s. In the present contribution we compare numerous graph parameters in the three largest lobes—frontal, parietal, temporal—and in both hemispheres of the human brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are held for the left frontal versus the right frontal and the right temporal versus the left temporal lobes.
-
The Frequent Subgraphs of the Connectome of the Human Brain
arXiv: Neurons and Cognition, 2017Co-Authors: Máté Fellner, Bálint Varga, Vince GrolmuszAbstract:In mapping the human structural Connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (i) discovery that women's Connectomes have deeper and richer connectivity-related graph parameters like those of men, or (ii) the description of more and less conservatively connected lobes and cerebral regions, and (iii) the discovery of the phenomenon of the Consensus Connectome Dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and functions. The present contribution describes the frequent connected subgraphs (instead of sub-circuits) of at most 6 edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected sub-graphs that are more frequent in female or the male Connectome. While our results describe subgraphs, instead of sub-circuits, we need to note that all macroscopic sub-circuits correspond to an underlying connected subgraph. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.
David B. Dunson - One of the best experts on this subject based on the ideXlab platform.
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Tensor network factorizations: Relationships between brain structural Connectomes and traits.
NeuroImage, 2019Co-Authors: Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. DunsonAbstract:Advanced brain imaging techniques make it possible to measure individuals' structural Connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural Connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural Connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural Connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural Connectome extraction pipeline produces a set of Connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural Connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural Connectomes and analysis method can give superior prediction accuracies compared with alternative Connectome constructions and other tensor and network regression methods.
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Tensor network factorizations: Relationships between brain structural Connectomes and traits
arXiv: Applications, 2018Co-Authors: Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. DunsonAbstract:Advanced brain imaging techniques make it possible to measure individuals' structural Connectomes in large cohort studies non-invasively. The structural Connectome is initially shaped by genetics and subsequently refined by the environment. It is extremely interesting to study relationships between structural Connectomes and environment factors or human traits, such as substance use and cognition. Due to limitations in structural Connectome recovery, previous studies largely focus on functional Connectomes. Questions remain about how well structural Connectomes can explain variance in different human traits. Using a state-of-the-art structural Connectome processing pipeline and a novel dimensionality reduction technique applied to data from the Human Connectome Project (HCP), we show strong relationships between structural Connectomes and various human traits. Our dimensionality reduction approach uses a tensor characterization of the Connectome and relies on a generalization of principal components analysis. We analyze over 1100 scans for 1076 subjects from the HCP and the Sherbrooke test-retest data set, as well as $175$ human traits that measure domains including cognition, substance use, motor, sensory and emotion. We find that structural Connectomes are associated with many traits. Specifically, fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity.
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Mapping Population-based Structural Connectomes
NeuroImage, 2018Co-Authors: Zhengwu Zhang, David B. Dunson, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, Anuj Srivastava, Hongtu ZhuAbstract:Abstract Advances in understanding the structural Connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural Connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects’ brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural Connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain Connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural Connectomes in healthy subjects.
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Relationships between Human Brain Structural Connectomes and Traits
2018Co-Authors: Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. DunsonAbstract:Advanced brain imaging techniques make it possible to measure individuals9 structural Connectomes in large cohort studies non-invasively. However, due to limitations in image resolution and pre-processing, questions remain about whether reconstructed Connectomes are measured accurately enough to detect relationships with human traits and behaviors. Using a state-of-the-art structural Connectome processing pipeline and a novel dimensionality reduction technique applied to data from the Human Connectome Project (HCP), we show strong relationships between Connectome structure and various human traits. Our dimensionality reduction approach uses a tensor characterization of the Connectomes and relies on a generalization of principal components analysis. We analyze over 1100 scans for 1076 subjects from the HCP and the Sherbrooke test-retest data set as well as 175 human traits that measure domains including cognition, substance use, motor, sensory and emotion. We find that brain Connectomes are associated with many traits. Specifically, fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity.
Zhengwu Zhang - One of the best experts on this subject based on the ideXlab platform.
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Tensor network factorizations: Relationships between brain structural Connectomes and traits.
NeuroImage, 2019Co-Authors: Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. DunsonAbstract:Advanced brain imaging techniques make it possible to measure individuals' structural Connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural Connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural Connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural Connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural Connectome extraction pipeline produces a set of Connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural Connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural Connectomes and analysis method can give superior prediction accuracies compared with alternative Connectome constructions and other tensor and network regression methods.
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Tensor network factorizations: Relationships between brain structural Connectomes and traits
arXiv: Applications, 2018Co-Authors: Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. DunsonAbstract:Advanced brain imaging techniques make it possible to measure individuals' structural Connectomes in large cohort studies non-invasively. The structural Connectome is initially shaped by genetics and subsequently refined by the environment. It is extremely interesting to study relationships between structural Connectomes and environment factors or human traits, such as substance use and cognition. Due to limitations in structural Connectome recovery, previous studies largely focus on functional Connectomes. Questions remain about how well structural Connectomes can explain variance in different human traits. Using a state-of-the-art structural Connectome processing pipeline and a novel dimensionality reduction technique applied to data from the Human Connectome Project (HCP), we show strong relationships between structural Connectomes and various human traits. Our dimensionality reduction approach uses a tensor characterization of the Connectome and relies on a generalization of principal components analysis. We analyze over 1100 scans for 1076 subjects from the HCP and the Sherbrooke test-retest data set, as well as $175$ human traits that measure domains including cognition, substance use, motor, sensory and emotion. We find that structural Connectomes are associated with many traits. Specifically, fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity.
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Mapping Population-based Structural Connectomes
NeuroImage, 2018Co-Authors: Zhengwu Zhang, David B. Dunson, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, Anuj Srivastava, Hongtu ZhuAbstract:Abstract Advances in understanding the structural Connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural Connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects’ brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural Connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain Connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural Connectomes in healthy subjects.
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Relationships between Human Brain Structural Connectomes and Traits
2018Co-Authors: Zhengwu Zhang, Genevera I. Allen, Hongtu Zhu, David B. DunsonAbstract:Advanced brain imaging techniques make it possible to measure individuals9 structural Connectomes in large cohort studies non-invasively. However, due to limitations in image resolution and pre-processing, questions remain about whether reconstructed Connectomes are measured accurately enough to detect relationships with human traits and behaviors. Using a state-of-the-art structural Connectome processing pipeline and a novel dimensionality reduction technique applied to data from the Human Connectome Project (HCP), we show strong relationships between Connectome structure and various human traits. Our dimensionality reduction approach uses a tensor characterization of the Connectomes and relies on a generalization of principal components analysis. We analyze over 1100 scans for 1076 subjects from the HCP and the Sherbrooke test-retest data set as well as 175 human traits that measure domains including cognition, substance use, motor, sensory and emotion. We find that brain Connectomes are associated with many traits. Specifically, fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity.