In the past few decades, behavioral and cognitive science have demonstrated that many human behav... more In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.
We report results from a dual electroencephalography (EEG) study, in which two-member teams perfo... more We report results from a dual electroencephalography (EEG) study, in which two-member teams performed a simulated combat scenario. Our aim was to distinguish expert from novice teams by their brain dynamics. Our findings suggest that dimensionality increases in the joint brain dynamics of the team members is a signature of increased task demand, both objective, e.g. increased task difficulty, and subjective, e.g. lack of experience in performing the task. Furthermore in each team we identified a subspace of joint brain dynamics related to team coordination. Our approach identifies signatures specific to team coordination by introducing surrogate team data as a baseline for joint brain dynamics without team coordination. This revealed that team coordination affects the subspace itself in which the joint brain dynamics of the team members are evolving, but not its dimensionality. Our results confirm the possibility to identify signatures of team coordination from the team members’ brain dynamics.
Clusters of correlated activity in fMRI data can identify regions of interest and indicate intera... more Clusters of correlated activity in fMRI data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on Hopfield networks. It allows to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. Further we propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations.
We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and corr... more We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus- independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabil- ities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.
Quantitative descriptors of intrinsic properties of fMRI data can be obtained from the theory of ... more Quantitative descriptors of intrinsic properties of fMRI data can be obtained from the theory of random matrices. We study data reduction based on the comparison of empirical correlation matrices with a suitably chosen ensemble of random positive matrices. Accordingly, data dimensions can be discarded if the quality of fit of the data spectrum deviates locally from the theoretical result, which is derived here analytically. Further, more complex quantities such as the number variance are discussed and shown to be potentially useful in an analogous manner.
Clusters of correlated activity in functional magnetic resonance imaging data can identify region... more Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity.
We investigate the influence of physiological measures like heart beat and respiration on functio... more We investigate the influence of physiological measures like heart beat and respiration on functional connectivity networks from fMRI. Cardiac and respiratory effects were measured simultaneously during high rate MRI data acquisition and the functional connectivity networks were determined in a data driven manner using graph theory. One of our findings is that removing the physiological effects from the data leads
Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the... more Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, eg in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.
In the past few decades, behavioral and cognitive science have demonstrated that many human behav... more In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.
We report results from a dual electroencephalography (EEG) study, in which two-member teams perfo... more We report results from a dual electroencephalography (EEG) study, in which two-member teams performed a simulated combat scenario. Our aim was to distinguish expert from novice teams by their brain dynamics. Our findings suggest that dimensionality increases in the joint brain dynamics of the team members is a signature of increased task demand, both objective, e.g. increased task difficulty, and subjective, e.g. lack of experience in performing the task. Furthermore in each team we identified a subspace of joint brain dynamics related to team coordination. Our approach identifies signatures specific to team coordination by introducing surrogate team data as a baseline for joint brain dynamics without team coordination. This revealed that team coordination affects the subspace itself in which the joint brain dynamics of the team members are evolving, but not its dimensionality. Our results confirm the possibility to identify signatures of team coordination from the team members’ brain dynamics.
Clusters of correlated activity in fMRI data can identify regions of interest and indicate intera... more Clusters of correlated activity in fMRI data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on Hopfield networks. It allows to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. Further we propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations.
We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and corr... more We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus- independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabil- ities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.
Quantitative descriptors of intrinsic properties of fMRI data can be obtained from the theory of ... more Quantitative descriptors of intrinsic properties of fMRI data can be obtained from the theory of random matrices. We study data reduction based on the comparison of empirical correlation matrices with a suitably chosen ensemble of random positive matrices. Accordingly, data dimensions can be discarded if the quality of fit of the data spectrum deviates locally from the theoretical result, which is derived here analytically. Further, more complex quantities such as the number variance are discussed and shown to be potentially useful in an analogous manner.
Clusters of correlated activity in functional magnetic resonance imaging data can identify region... more Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity.
We investigate the influence of physiological measures like heart beat and respiration on functio... more We investigate the influence of physiological measures like heart beat and respiration on functional connectivity networks from fMRI. Cardiac and respiratory effects were measured simultaneously during high rate MRI data acquisition and the functional connectivity networks were determined in a data driven manner using graph theory. One of our findings is that removing the physiological effects from the data leads
Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the... more Functional Magnetic Resonance Imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity under a given paradigm, eg in response to certain stimuli. In the context of a motor task experiment we discuss methods for analyzing fMRI data based on principal and independent component analysis with respect to their capabilities of separating noise sources from functional activity.
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Papers by Silke Dodel