Matching Items (4)
Description
Neural activity tracking using electroencephalography (EEG) and magnetoencephalography (MEG) brain scanning methods has been widely used in the field of neuroscience to provide insight into the nervous system. However, the tracking accuracy depends on the presence of artifacts in the EEG/MEG recordings. Artifacts include any signals that do not originate from neural activity, including physiological artifacts such as eye movement and non-physiological activity caused by the environment.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
ContributorsJiang, Jiewei (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
Neuroimaging has appeared in the courtroom as a type of `evidence' to support claims about whether or not criminals should be held accountable for their crimes. Yet the ability to abstract notions of culpability and criminal behavior with confidence from these imagines is unclear. As there remains much to be discovered in the relationship between personal responsibility, criminal behavior, and neurological abnormalities, questions have been raised toward neuroimaging as an appropriate means to validate these claims.
This project explores the limits and legitimacy of neuroimaging as a means of understanding behavior and culpability in determining appropriate criminal sentencing. It highlights key philosophical issues surrounding the ability to use neuroimaging to support this process, and proposes a method of ensuring their proper use. By engaging case studies and a thought experiment, this project illustrates the circumstances in which neuroimaging may assist in identifying particular characteristics relevant for criminal sentencing.
I argue that it is not a question of whether or not neuroimaging itself holds validity in determining a criminals guilt or motives, but rather a proper application of the issue is to focus on the way in which information regarding these images is communicated from the `expert' scientists to the `non-expert' making decisions about the sentence that are most important. Those who are considering this information's relevance, a judge or jury, are typically not well versed in criminal neuroscience and interpreting the significance of different images. I advocate the way in which this information is communicated from the scientist-informer to the decision-maker parallels in importance to its actual meaning.
As a solution, I engage Roger Pielke's model of honest brokering as a solution to ensure the appropriate use of neuroimaging in determining criminal responsibility and sentencing. A thought experiment follows to highlight the limits of science, engage philosophical repercussions, and illustrate honest brokering as a means of resolution. To achieve this, a hypothetical dialogue reminiscent of Kenneth Schaffner's `tools for talking' with behavioral geneticists and courtroom professionals will exemplify these ideas.
This project explores the limits and legitimacy of neuroimaging as a means of understanding behavior and culpability in determining appropriate criminal sentencing. It highlights key philosophical issues surrounding the ability to use neuroimaging to support this process, and proposes a method of ensuring their proper use. By engaging case studies and a thought experiment, this project illustrates the circumstances in which neuroimaging may assist in identifying particular characteristics relevant for criminal sentencing.
I argue that it is not a question of whether or not neuroimaging itself holds validity in determining a criminals guilt or motives, but rather a proper application of the issue is to focus on the way in which information regarding these images is communicated from the `expert' scientists to the `non-expert' making decisions about the sentence that are most important. Those who are considering this information's relevance, a judge or jury, are typically not well versed in criminal neuroscience and interpreting the significance of different images. I advocate the way in which this information is communicated from the scientist-informer to the decision-maker parallels in importance to its actual meaning.
As a solution, I engage Roger Pielke's model of honest brokering as a solution to ensure the appropriate use of neuroimaging in determining criminal responsibility and sentencing. A thought experiment follows to highlight the limits of science, engage philosophical repercussions, and illustrate honest brokering as a means of resolution. To achieve this, a hypothetical dialogue reminiscent of Kenneth Schaffner's `tools for talking' with behavioral geneticists and courtroom professionals will exemplify these ideas.
ContributorsTaddeo, Sarah (Author) / Robert, Jason S (Thesis advisor) / Marchant, Gary (Committee member) / Hurlbut, James B (Committee member) / Arizona State University (Publisher)
Created2014
Description
There is conflicting evidence regarding whether a biasing effect of neuroscientific evidence exists. Early research warned of such bias, but more recent papers dispute such claims, with some suggesting a bias only occurs in situations of relative judgment, but not in situations of absolute judgment. The current studies examined the neuroimage bias within both criminal and civil court case contexts, specifically exploring if a bias is dependent on the context in which the neuroimage evidence is presented (i.e. a single expert vs. opposing experts). In the first experiment 408 participants read a criminal court case summary in which either one expert witness testified (absolute judgment) or two experts testified (relative judgment). The experts presented neurological evidence in the form of functional magnetic resonance imaging (fMRI) data and the evidence type varied between a brain image and a graph. A neuroimage bias was found, in that jurors who were exposed to two experts were more punitive when the prosecution presented the image and less punitive when the defense did. In the second experiment 240 participants read a summary of a civil court case in which either a single expert witness testified or two experts testified. The experts presented fMRI data to support or refute a claim of chronic pain and the evidence type again varied between image and graph. The expected neuroimage bias was not found, in that jurors were more likely to find in favor of the plaintiff when either side proffered the image, but more likely to find for the defense when only graphs were offered by the experts. These findings suggest that the introduction of neuroimages as evidence may affect jurors punitiveness in criminal cases, as well as liability decisions in civil cases and overall serves to illustrate that the influence of neuroscientific information on legal decision makers is more complex than originally thought.
ContributorsHafdahl, Riquel J (Author) / Schweitzer, Nicholas (Thesis advisor) / Salerno, Jessica (Committee member) / Neal, Tess (Committee member) / Arizona State University (Publisher)
Created2016
Description
In brain imaging study, 3D surface-based algorithms may provide more advantages over volume-based methods, due to their sub-voxel accuracy to represent subtle subregional changes and solid mathematical foundations on which global shape analyses can be achieved on complicated topological structures, such as the convoluted cortical surfaces. On the other hand, given the enormous amount of data being generated daily, it is still challenging to develop effective and efficient surface-based methods to analyze brain shape morphometry. There are two major problems in surface-based shape analysis research: correspondence and similarity. This dissertation covers both topics by proposing novel surface registration and indexing algorithms based on conformal geometry for brain morphometry analysis.
First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).
Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.
Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).
Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.
Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
ContributorsShi, Jie, Ph.D (Author) / Wang, Yalin (Thesis advisor) / Caselli, Richard (Committee member) / Li, Baoxin (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2016