subject:(Brain Connectivity)

1. Demiralp, Cagatay. Computational Brain Connectivity Using Diffusion MRI.

Degree: PhD, Computer Science, 2012, Brown University

URL: https://repository.library.brown.edu/studio/item/bdr:320512/

► This dissertation shows that qualitative and quantitative characterization of patterned structures in brain connectivity data obtained using diffusion MRI not only improves the exploration of… (more)

▼ This dissertation shows that qualitative and quantitative characterization of patterned structures in brain connectivity data obtained using diffusion MRI not only improves the exploration of the intricate space of brain connectivity but also provides clinically meaningful measures, quantifying normal and pathological variation in the brain. To this end, we introduce a set of computational and mathematical models, algorithms, and interactive tools to explore, understand, and characterize diffusion-derived structural brain connectivity. We contribute to all stages of modeling, visualization, and analysis of brain connectivity. In modeling, our contributions are twofold. First, we model the joint distribution of local neural fiber configurations with Markov random fields and infer the most likely configuration with maximum a posteriori estimation. We demonstrate this framework's use in resolving fiber crossings. Second, we introduce new planar map representations of three-dimensional neural tract datasets. These planar representations improve the exploration of brain connectivity by reducing visual and interaction complexity. In visualization, we contribute to structure-preserving color mappings. First, we introduce Boy's surface as a model for coloring 3D line fields and show results from its application in visualizing orientation in diffusion MRI brain datasets. This coloring method is smooth and one-to-one except on a set of measure zero. Second, we propose a general coloring method based on manifold embedding that conveys spatial relations among neural fiber tracts perceptually. We also introduce a new bivariate coloring model, the flat torus, that allows finer adjustments of coloring arbitrarily. We contribute to both local and global analysis of brain connectivity. In local analysis, we introduce a geometric slicing-based coherence measure for clusters of neural tracts. Clustering refinement based on this measure leads to a significant improvement in clustering quality that is not possible directly with standard methods. We also introduce tract-based probability density functions and demonstrate their effective use in nonparametric hypothesis testing and classification. In global analysis, we propose computing the ranks of persistent homology groups in the neural tract space. This captures the effects of diffuse axonal dropout and provides a global descriptor of structural brain connectivity. Advisors/Committee Members: Laidlaw, David (Director), Hughes, John (Reader), Mumford, David (Reader).

Subjects/Keywords: Computational brain connectivity

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APA (6^th Edition):

Demiralp, C. (2012). Computational Brain Connectivity Using Diffusion MRI. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:320512/

Chicago Manual of Style (16^th Edition):

Demiralp, Cagatay. “Computational Brain Connectivity Using Diffusion MRI.” 2012. Doctoral Dissertation, Brown University. Accessed January 19, 2021. https://repository.library.brown.edu/studio/item/bdr:320512/.

MLA Handbook (7^th Edition):

Demiralp, Cagatay. “Computational Brain Connectivity Using Diffusion MRI.” 2012. Web. 19 Jan 2021.

Vancouver:

Demiralp C. Computational Brain Connectivity Using Diffusion MRI. [Internet] [Doctoral dissertation]. Brown University; 2012. [cited 2021 Jan 19]. Available from: https://repository.library.brown.edu/studio/item/bdr:320512/.

Council of Science Editors:

Demiralp C. Computational Brain Connectivity Using Diffusion MRI. [Doctoral Dissertation]. Brown University; 2012. Available from: https://repository.library.brown.edu/studio/item/bdr:320512/

Vanderbilt University

2. Fan, Qiuyun. Diffusion Tensor Imaging reveals correlations between brain connectivity and children's reading abilities.

Degree: MS, Biomedical Engineering, 2011, Vanderbilt University

URL: http://hdl.handle.net/1803/11518

► This study demonstrated the relationship between brain connectivity and children’s reading abilities. For the behavioral part, the participants received proper reading interventions based on their… (more)

Subjects/Keywords: DTI; brain connectivity; reading

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APA (6^th Edition):

Fan, Q. (2011). Diffusion Tensor Imaging reveals correlations between brain connectivity and children's reading abilities. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11518

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Fan, Qiuyun. “Diffusion Tensor Imaging reveals correlations between brain connectivity and children's reading abilities.” 2011. Thesis, Vanderbilt University. Accessed January 19, 2021. http://hdl.handle.net/1803/11518.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Fan, Qiuyun. “Diffusion Tensor Imaging reveals correlations between brain connectivity and children's reading abilities.” 2011. Web. 19 Jan 2021.

Vancouver:

Fan Q. Diffusion Tensor Imaging reveals correlations between brain connectivity and children's reading abilities. [Internet] [Thesis]. Vanderbilt University; 2011. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1803/11518.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fan Q. Diffusion Tensor Imaging reveals correlations between brain connectivity and children's reading abilities. [Thesis]. Vanderbilt University; 2011. Available from: http://hdl.handle.net/1803/11518

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

3. Aydore, Sergul. Measuring functional connectivity of the brain.

Degree: PhD, Electrical Engineering, 2015, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/468390/rec/3996

► The rich temporal content of measurements of electromagnetic activity, including electroencephalography (EEG) and magnetoencephalography (MEG), allow researchers to study dynamic functional networks in the human… (more)

▼ The rich temporal content of measurements of electromagnetic activity, including electroencephalography (EEG) and magnetoencephalography (MEG), allow researchers to study dynamic functional networks in the human brain. However, it is difficult to turn this data into meaningful conclusions about those brain networks. In this dissertation, we describe the theoretical relationships between different interaction measures, followed by development of novel measures to address classical nuisance of cross-talk in brain electrophysiological recordings. ❧ Coherence and phase locking value (PLV) are widely used measures that can reveal interactions between electrophysiological signals within a frequency range of interest. We investigate the statistical properties of the PLV by describing two distributions that are widely used to a priori model phase interactions. The first of these is the von Mises distribution, for which the standard sample PLV is a maximum likelihood estimator. The second is the relative phase distribution derived from bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the coherence between the two signals. We then compare results via local field potential data from a visually-cued motor study in macaque for the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the coherence of the two complex time series. This result reduces the analysis of time-locked activity between signals to the computation of coherence rather than coherence and PLV. ❧ Since the PLV is a bivariate measure (that is, it is computed pairwise between signals) it cannot differentiate between direct and indirect connections in a multidimensional network. A non-parametric partial phase synchronization index attempted to resolve this problem by extending sample PLV to the multivariate case using the same mechanism relating correlation to partial correlation. Here we derive an analytical expression for partial PLV for a multivariate circular complex Gaussian model and show that partial PLV can be computed from partial coherence. We demonstrate our method in simulations with Roessler oscillators and experimental data of multichannel local field potentials from a macaque monkey. We show that the multivariate circular complex Gaussian model suggests similar synchronization networks. In addition, the circular complex Gaussian model has a lower variance in the estimation of partial PLV. ❧ Interpretation of functional connectivity from EEG/MEG data is challenging due to cross-talk problem between signals of interest. For example, coherence may yield spuriously large values leading false positive connections. Approaches such as imaginary coherence, phase lag index, lagged coherence and orthogonal coherence have been proposed to overcome this problem. The common assumption of these measures is that time-lagged interactions are more robust to cross-talk than instantaneous interactions.… Advisors/Committee Members: Leahy, Richard M. (Committee Chair), Haldar, Justin P. (Committee Member), Nayak, Krishna S. (Committee Member).

Subjects/Keywords: functional connectivity; brain; EEG; MEG

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APA (6^th Edition):

Aydore, S. (2015). Measuring functional connectivity of the brain. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/468390/rec/3996

Chicago Manual of Style (16^th Edition):

Aydore, Sergul. “Measuring functional connectivity of the brain.” 2015. Doctoral Dissertation, University of Southern California. Accessed January 19, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/468390/rec/3996.

MLA Handbook (7^th Edition):

Aydore, Sergul. “Measuring functional connectivity of the brain.” 2015. Web. 19 Jan 2021.

Vancouver:

Aydore S. Measuring functional connectivity of the brain. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2021 Jan 19]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/468390/rec/3996.

Council of Science Editors:

Aydore S. Measuring functional connectivity of the brain. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/468390/rec/3996

University of Melbourne

4. Harding, Ian Herbert. Specialization and integration in brain networks underlying cognitive control in healthy individuals and patients with schizophrenia.

Degree: 2013, University of Melbourne

URL: http://hdl.handle.net/11343/38203

► Cognitive control lies at the foundation of dynamic and adaptive human behaviour. Through the flexible top-down regulation of lower-order processes, cognitive control operations serve to… (more)

▼ Cognitive control lies at the foundation of dynamic and adaptive human behaviour. Through the flexible top-down regulation of lower-order processes, cognitive control operations serve to direct the perceptual, motor, and other cognitive resources of the brain in response to ever changing environmental demands and behavioural goals. These abilities, including cognitive interference resolution and working memory operations, rely on a common set of brain regions located within the prefrontal and parietal association cortices, together forming the frontoparietal control network. The component regions of this network are variously responsible for encoding and updating goal and context representations, signalling motivational salience, monitoring action-outcomes, and discriminating amongst ambiguous perceptual information and behavioural contingencies. Meaningful and coherent behaviour is dependent both on information processing within each of these regions (specialization) and the amalgamation of function across the network (integration). Although the frontoparietal control network is well defined and has been widely investigated, little is yet known about how it operates when faced with multiple concurrent control demands, as would be expected in real-world environments. Moreover, the shared and unique nature of connectivity patterns within this common brain network across different cognitive control processes is currently unknown. In schizophrenia patients, dysfunction in cognitive control abilities is endemic and is thought to lie at the core of the significant disability faced by patients suffering from the illness. Current theories variously propose that abnormalities in the integrity and efficiency of neural functioning, as well as in the normal integration of activity within the frontoparietal control network may underlie these deficits. This thesis presents a series of experiments exploring the activation and connectivity patterns defining the frontoparietal network as a function of distinct cognitive control demands. Functional magnetic resonance imaging (fMRI) data was acquired during performance of a novel cognitive paradigm in which cognitive interference and working memory demands were manipulated in a factorial manner. Functional activations and effective connectivity were assessed using statistical parametric mapping (SPM) and dynamic causal modelling (DCM) techniques, respectively. Investigations were first undertaken in a group of healthy adults, and followed thereafter by a characterization of differences evident in a cohort of patients suffering from schizophrenia. Taken together, the frontoparietal system was found to be highly adaptable, widely interconnected, and characterized by both common and unique dynamics in response to different cognitive control demands. These…

Subjects/Keywords: cognition; brain imaging; brain connectivity; schizophrenia

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APA (6^th Edition):

Harding, I. H. (2013). Specialization and integration in brain networks underlying cognitive control in healthy individuals and patients with schizophrenia. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/38203

Chicago Manual of Style (16^th Edition):

Harding, Ian Herbert. “Specialization and integration in brain networks underlying cognitive control in healthy individuals and patients with schizophrenia.” 2013. Doctoral Dissertation, University of Melbourne. Accessed January 19, 2021. http://hdl.handle.net/11343/38203.

MLA Handbook (7^th Edition):

Harding, Ian Herbert. “Specialization and integration in brain networks underlying cognitive control in healthy individuals and patients with schizophrenia.” 2013. Web. 19 Jan 2021.

Vancouver:

Harding IH. Specialization and integration in brain networks underlying cognitive control in healthy individuals and patients with schizophrenia. [Internet] [Doctoral dissertation]. University of Melbourne; 2013. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/11343/38203.

Council of Science Editors:

Harding IH. Specialization and integration in brain networks underlying cognitive control in healthy individuals and patients with schizophrenia. [Doctoral Dissertation]. University of Melbourne; 2013. Available from: http://hdl.handle.net/11343/38203

University of Sydney

5. Pandejee, Grishma Riken. Prediction and Analysis of Connectivity in the Brain .

Degree: 2018, University of Sydney

URL: http://hdl.handle.net/2123/18179

► Neural brain connectivity has three aspects: a physical connection between different brain regions – termed anatomical connectivity; a mutual relationship between dynamical activities between different… (more)

Subjects/Keywords: Functional connectivity; Effective connectivity; Anatomical connectivity; Multistep connections; Brain lesion; includes published articles

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APA (6^th Edition):

Pandejee, G. R. (2018). Prediction and Analysis of Connectivity in the Brain . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/18179

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Pandejee, Grishma Riken. “Prediction and Analysis of Connectivity in the Brain .” 2018. Thesis, University of Sydney. Accessed January 19, 2021. http://hdl.handle.net/2123/18179.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Pandejee, Grishma Riken. “Prediction and Analysis of Connectivity in the Brain .” 2018. Web. 19 Jan 2021.

Vancouver:

Pandejee GR. Prediction and Analysis of Connectivity in the Brain . [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2123/18179.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pandejee GR. Prediction and Analysis of Connectivity in the Brain . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/18179

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Utah

6. Liu, Wei. Resting state functional magnetic resonance imaging analysis by graphical model.

Degree: PhD, Computing (School of), 2014, University of Utah

URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102

► Functional magnetic resonance imaging (fMRI) measures the change of oxygen consumption level in the blood vessels of the human brain, hence indirectly detecting the neuronal… (more)

▼ Functional magnetic resonance imaging (fMRI) measures the change of oxygen consumption level in the blood vessels of the human brain, hence indirectly detecting the neuronal activity. Resting-state fMRI (rs-fMRI) is used to identify the intrinsic functional patternsof the brain when there is no external stimulus. Accurate estimation of intrinsic activity is important for understanding the functional organization and dynamics of the brain, as well as differences in the functional networks of patients with mental disorders.This dissertation aims to robustly estimate the functional connectivities and networks of the human brain using rs-fMRI data of multiple subjects. We use Markov random field (MRF), an undirected graphical model to represent the statistical dependency among thefunctional network variables. Graphical models describe multivariate probability distributions that can be factorized and represented by a graph. By defining the nodes and the edges along with their weights according to our assumptions, we build soft constraints into thegraph structure as prior information. We explore various approximate optimization methods including variational Bayesian, graph cuts, and Markov chain Monte Carlo sampling (MCMC).We develop the random field models to solve three related problems. In the first problem, the goal is to detect the pairwise connectivity between gray matter voxels in a rs-fMRI dataset of the single subject. We define a six-dimensional graph to represent our priorinformation that two voxels are more likely to be connected if their spatial neighbors are connected. The posterior mean of the connectivity variables are estimated by variational inference, also known as mean field theory in statistical physics. The proposed methodproves to outperform the standard spatial smoothing and is able to detect finer patterns of brain activity. Our second work aims to identify multiple functional systems. We define a Potts model, a special case of MRF, on the network label variables, and define von Mises-Fisher distribution on the normalized fMRI signal. The inference is significantly more difficult than the binary classification in the previous problem. We use MCMC to draw samples from the posterior distribution of network labels. In the third application, we extend the graphical model to the multiple subject scenario. By building a graph including the network labels of both a group map and the subject label maps, we define a hierarchical model that has richer structure than the flat single-subject model, and captures the shared patterns as well as the variation among the subjects. All three solutions are data-drivenBayesian methods, which estimate model parameters from the data. The experiments show that by the regularization of MRF, the functional network maps we estimate are more accurate and more consistent across multiple sessions.

Subjects/Keywords: Brain connectivity; Functional MRI; Graphical models

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APA (6^th Edition):

Liu, W. (2014). Resting state functional magnetic resonance imaging analysis by graphical model. (Doctoral Dissertation). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102

Chicago Manual of Style (16^th Edition):

Liu, Wei. “Resting state functional magnetic resonance imaging analysis by graphical model.” 2014. Doctoral Dissertation, University of Utah. Accessed January 19, 2021. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102.

MLA Handbook (7^th Edition):

Liu, Wei. “Resting state functional magnetic resonance imaging analysis by graphical model.” 2014. Web. 19 Jan 2021.

Vancouver:

Liu W. Resting state functional magnetic resonance imaging analysis by graphical model. [Internet] [Doctoral dissertation]. University of Utah; 2014. [cited 2021 Jan 19]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102.

Council of Science Editors:

Liu W. Resting state functional magnetic resonance imaging analysis by graphical model. [Doctoral Dissertation]. University of Utah; 2014. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102

University of Cincinnati

7. KARUNANAYAKA, PRASANNA RASIKA. BRAIN CONNECTIVITY ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE DATA FOR STORY COMPREHENSION IN CHILDREN USING GROUP INDEPENDENT COMPONENT ANALYSIS AND STRUCTURAL EQUATION MODELING.

Degree: MS, Engineering : Computer Science, 2007, University of Cincinnati

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172599790

► Structural Equation Modeling (SEM) is combined with Group Independent Component Analysis (ICA) to investigate the developmental trends in brain connectivity coefficients associated with the human… (more)

Subjects/Keywords: Brain Connectivity Analysis

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APA (6^th Edition):

KARUNANAYAKA, P. R. (2007). BRAIN CONNECTIVITY ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE DATA FOR STORY COMPREHENSION IN CHILDREN USING GROUP INDEPENDENT COMPONENT ANALYSIS AND STRUCTURAL EQUATION MODELING. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172599790

Chicago Manual of Style (16^th Edition):

KARUNANAYAKA, PRASANNA RASIKA. “BRAIN CONNECTIVITY ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE DATA FOR STORY COMPREHENSION IN CHILDREN USING GROUP INDEPENDENT COMPONENT ANALYSIS AND STRUCTURAL EQUATION MODELING.” 2007. Masters Thesis, University of Cincinnati. Accessed January 19, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172599790.

MLA Handbook (7^th Edition):

KARUNANAYAKA, PRASANNA RASIKA. “BRAIN CONNECTIVITY ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE DATA FOR STORY COMPREHENSION IN CHILDREN USING GROUP INDEPENDENT COMPONENT ANALYSIS AND STRUCTURAL EQUATION MODELING.” 2007. Web. 19 Jan 2021.

Vancouver:

KARUNANAYAKA PR. BRAIN CONNECTIVITY ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE DATA FOR STORY COMPREHENSION IN CHILDREN USING GROUP INDEPENDENT COMPONENT ANALYSIS AND STRUCTURAL EQUATION MODELING. [Internet] [Masters thesis]. University of Cincinnati; 2007. [cited 2021 Jan 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172599790.

Council of Science Editors:

KARUNANAYAKA PR. BRAIN CONNECTIVITY ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE DATA FOR STORY COMPREHENSION IN CHILDREN USING GROUP INDEPENDENT COMPONENT ANALYSIS AND STRUCTURAL EQUATION MODELING. [Masters Thesis]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172599790

Oklahoma State University

8. Ratliff, Erin L. Role of Cross-Brain Connectivity in Emotion Regulation Within the Parent-Adolescent Dyad.

Degree: Human Development and Family Science, 2019, Oklahoma State University

URL: http://hdl.handle.net/11244/323421

► Emotion regulation is influential in adolescent mental health outcomes. Specifically, poor emotion regulation skills and strategies have been shown to be related to increased rates… (more)

Subjects/Keywords: adolescents; cross-brain connectivity; emotion regulation; parenting

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APA (6^th Edition):

Ratliff, E. L. (2019). Role of Cross-Brain Connectivity in Emotion Regulation Within the Parent-Adolescent Dyad. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/323421

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ratliff, Erin L. “Role of Cross-Brain Connectivity in Emotion Regulation Within the Parent-Adolescent Dyad.” 2019. Thesis, Oklahoma State University. Accessed January 19, 2021. http://hdl.handle.net/11244/323421.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ratliff, Erin L. “Role of Cross-Brain Connectivity in Emotion Regulation Within the Parent-Adolescent Dyad.” 2019. Web. 19 Jan 2021.

Vancouver:

Ratliff EL. Role of Cross-Brain Connectivity in Emotion Regulation Within the Parent-Adolescent Dyad. [Internet] [Thesis]. Oklahoma State University; 2019. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/11244/323421.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ratliff EL. Role of Cross-Brain Connectivity in Emotion Regulation Within the Parent-Adolescent Dyad. [Thesis]. Oklahoma State University; 2019. Available from: http://hdl.handle.net/11244/323421

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

9. Leming, Matthew. Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets.

Degree: PhD, 2020, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/311070

► The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain… (more)

▼ The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.

Subjects/Keywords: Deep Learning; Functional MRI; Brain Connectivity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Leming, M. (2020). Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/311070

Chicago Manual of Style (16^th Edition):

Leming, Matthew. “Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 19, 2021. https://www.repository.cam.ac.uk/handle/1810/311070.

MLA Handbook (7^th Edition):

Leming, Matthew. “Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets.” 2020. Web. 19 Jan 2021.

Vancouver:

Leming M. Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 19]. Available from: https://www.repository.cam.ac.uk/handle/1810/311070.

Council of Science Editors:

Leming M. Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/311070

University of Cambridge

10. Leming, Matthew. Application of deep learning to brain connectivity classification in large MRI datasets.

Degree: PhD, 2020, University of Cambridge

URL: https://doi.org/10.17863/CAM.58159 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818119

► The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain… (more)

▼ The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.

Subjects/Keywords: Deep Learning; Functional MRI; Brain Connectivity

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APA (6^th Edition):

Leming, M. (2020). Application of deep learning to brain connectivity classification in large MRI datasets. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.58159 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818119

Chicago Manual of Style (16^th Edition):

Leming, Matthew. “Application of deep learning to brain connectivity classification in large MRI datasets.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 19, 2021. https://doi.org/10.17863/CAM.58159 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818119.

MLA Handbook (7^th Edition):

Leming, Matthew. “Application of deep learning to brain connectivity classification in large MRI datasets.” 2020. Web. 19 Jan 2021.

Vancouver:

Leming M. Application of deep learning to brain connectivity classification in large MRI datasets. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 19]. Available from: https://doi.org/10.17863/CAM.58159 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818119.

Council of Science Editors:

Leming M. Application of deep learning to brain connectivity classification in large MRI datasets. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.58159 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.818119

University of New South Wales

11. Perry, Alistair. Brain networks in healthy ageing and psychiatric conditions.

Degree: Psychiatry, 2017, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/58844 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47732/SOURCE02?view=true

► Conceptualising the human brain upon its large-scale interactions has led to the realisation of integrativeneural processes as critical to cerebral functioning. This thesis sought to… (more)

▼ Conceptualising the human brain upon its large-scale interactions has led to the realisation of integrativeneural processes as critical to cerebral functioning. This thesis sought to elucidate the brain patterns offunctional integration and segregation that are associated with the cognitive and behavioural changes inhealthy ageing and psychiatric conditions. The network features expressed with age-related cognitivechanges are poorly understood within a healthy older population. Th e brain network disturbances inindividuals at high-genetic risk for bipolar disorder (BO) are also unknown.Study 1 (Chapter 2) leveraged advances in diffusion-tractography to derive the features of structuralbrain networks in healthy older adults. The integrative features of the core backbone are observed in theconnectomes of both young and older adults, reflecting ongoing patterns of efficient brain communication.Study 2 (Chapter 3) leveraged multivariate analysis to examine in healthy older adults the complexrelations between age, functional connectivity, and cognitive performance. A functional sensorimotorsubnetwork was identified whose expression is opposed by age against core cognitive processes such asattention and processing speed. Modifiable factors such as increased education are associated with distinctfunctional networks.Lastly, study 3 (Chapter 4) investigated the structural networks in patients and also unaffectedrelatives at high-genetic risk for BO. Relative to matched-controls, alterations to fronto-limbic circuitshousing key emotional and cognitive centers were identified within both patient and high-risk groups.The present works illustrate the expression of large-scale brain network features are associated withphenotypic differences in healthy older adults and psychiatric conditions. Inter-individual differences in theintegration of cerebral information processing is strongly implicated here for the respective changes infunctioning: Sensorimotor networks supporting lower-order processes are most sensitive to healthy ageing,whilst fronto-limbic disturbances in patient and high-risk groups are consistent with the emotional liabilityin BO. The integrative features of key-hub regions are also demonstrated throughout these studies ascritical to brain communication capacity. This thesis hence contributes as an important body of work in ourability to understand and predict human brain functioning and behaviour. Advisors/Committee Members: Wen, Wei, Psychiatry, Faculty of Medicine, UNSW.

Subjects/Keywords: Functional connectivity; Brain networks; Structural connectivity; Ageing; Bipolar disorder; Graph theory

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APA (6^th Edition):

Perry, A. (2017). Brain networks in healthy ageing and psychiatric conditions. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58844 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47732/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Perry, Alistair. “Brain networks in healthy ageing and psychiatric conditions.” 2017. Doctoral Dissertation, University of New South Wales. Accessed January 19, 2021. http://handle.unsw.edu.au/1959.4/58844 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47732/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Perry, Alistair. “Brain networks in healthy ageing and psychiatric conditions.” 2017. Web. 19 Jan 2021.

Vancouver:

Perry A. Brain networks in healthy ageing and psychiatric conditions. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2021 Jan 19]. Available from: http://handle.unsw.edu.au/1959.4/58844 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47732/SOURCE02?view=true.

Council of Science Editors:

Perry A. Brain networks in healthy ageing and psychiatric conditions. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58844 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47732/SOURCE02?view=true

University of Melbourne

12. ELLIS, RACHEL. Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence.

Degree: 2014, University of Melbourne

URL: http://hdl.handle.net/11343/42064

► Adolescence is a period of increased risk for the onset of depression, and evidence suggests that depressive episodes in adolescence increase the risk for further… (more)

▼ Adolescence is a period of increased risk for the onset of depression, and evidence suggests that depressive episodes in adolescence increase the risk for further episodes later in life. It follows that adolescence is a developmental window during which lifelong risk for depression may be shaped in the brain. The aims of this thesis were: 1) to identify longitudinal trajectories of depressive symptoms in a sample of adolescents, using growth mixture modelling; 2) to relate these trajectory groups to psychosocial risk factors and outcomes, and to neurobiological outcomes in young adulthood. A connectomic approach was used to examine brain structural connectivity and relate individual variation to depression trajectories. This research was conducted as part of the Orygen Adolescent Development Study (ADS), a prospective longitudinal study of Melbourne children. The analysis was based on a sample of 243 neurologically healthy adolescents (121 males and 122 females), who were recruited from a random selection of Melbourne schools. Participants were comprehensively assessed using a battery of measures of brain structure, temperament, family processes, and psychopathology. Data were obtained from four phases of data collection over eight years. Depression scores from each of the four time points were used to model latent class growth trajectories, and a four-group solution was selected as the best-fitting model: a normative group with ongoing stable low levels of depression, two groups with declining depressive symptoms, and one group with increasing symptoms across adolescence. Trajectory class was shown to be predictive of a range of psychosocial variables including temperament, childhood maltreatment, and young adult quality of life. Diffusion-weighted MRI brain images were acquired at the final time point, and used to perform white matter tractography and brain network analysis. Key topological properties of the resulting connectivity networks were identified. The four depression trajectory groups were tested for mean differences in brain connectivity variables at global and local levels of analysis. This analysis revealed no differences between the groups at the whole-brain level, but differences in several specific regions and connections, primarily in the corticolimbic network. The groups that had experienced elevated depression symptoms in early adolescence differed from the normative group in a greater number of areas than the group who had experienced depression later. The implications of these results are that earlier onset of mood disorder is associated with reduced efficiency in a greater number of brain regions. Early onset of depression has a lasting effect on brain structure, and affected tracts correspond to areas of white matter that are still maturing during this period, particularly frontolimbic and parietal regions.

Subjects/Keywords: adolescent depression; growth mixture modelling; brain connectivity; brain networks

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APA (6^th Edition):

ELLIS, R. (2014). Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/42064

Chicago Manual of Style (16^th Edition):

ELLIS, RACHEL. “Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence.” 2014. Doctoral Dissertation, University of Melbourne. Accessed January 19, 2021. http://hdl.handle.net/11343/42064.

MLA Handbook (7^th Edition):

ELLIS, RACHEL. “Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence.” 2014. Web. 19 Jan 2021.

Vancouver:

ELLIS R. Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence. [Internet] [Doctoral dissertation]. University of Melbourne; 2014. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/11343/42064.

Council of Science Editors:

ELLIS R. Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence. [Doctoral Dissertation]. University of Melbourne; 2014. Available from: http://hdl.handle.net/11343/42064

13. Amico, Enrico. Methods and models for brain connectivity assessment across levels of consciousness.

Degree: 2016, Ghent University

URL: http://hdl.handle.net/1854/LU-8070168

► The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of… (more)

▼ The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics. In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes. Indeed, the network approach for studying the brain has several advantages: 1) it eases the study of collective behaviors and interactions between regions; 2) allows to map and study quantitative properties of its anatomical pathways; 3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions. The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness). Advisors/Committee Members: Laureys, Steven, Marinazzo, Daniele.

Subjects/Keywords: Medicine and Health Sciences; Brain connectivity; brain networks; consciousness

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APA (6^th Edition):

Amico, E. (2016). Methods and models for brain connectivity assessment across levels of consciousness. (Thesis). Ghent University. Retrieved from http://hdl.handle.net/1854/LU-8070168

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Amico, Enrico. “Methods and models for brain connectivity assessment across levels of consciousness.” 2016. Thesis, Ghent University. Accessed January 19, 2021. http://hdl.handle.net/1854/LU-8070168.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Amico, Enrico. “Methods and models for brain connectivity assessment across levels of consciousness.” 2016. Web. 19 Jan 2021.

Vancouver:

Amico E. Methods and models for brain connectivity assessment across levels of consciousness. [Internet] [Thesis]. Ghent University; 2016. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1854/LU-8070168.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Amico E. Methods and models for brain connectivity assessment across levels of consciousness. [Thesis]. Ghent University; 2016. Available from: http://hdl.handle.net/1854/LU-8070168

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Oxford

14. Stevner, Angus Bror Andersen. Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep.

Degree: PhD, 2017, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:3ef218c0-a734-4d6f-abf8-ffdb780525aa ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748743

► This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically,… (more)

▼ This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically, I provide new information based on integrating spatial and temporal aspects of brain activity both in the transitions between and during wakefulness and various stages of non-rapid-eye movement (NREM) sleep. This is achieved through investigations of inter-regional interactions, functional connectivity (FC), between activity timecourses throughout the brain. Overall, the presented findings provide new important whole-brain insights for our current understanding of sleep, and potentially also of sleep disorders and consciousness in general. In Chapter 2 I present a robust global increase in similarity between the structural connectivity (SC) and the FC in slow-wave sleep (SWS) in almost all of the participants of two independent fMRI datasets. This could point to a decreased state repertoire and more rigid brain dynamics during SWS. Chapter 2 further identifies the changes in FC strengths between wakefulness and individual stages of NREM sleep across the whole-brain fMRI network. I report connectivity in posterior parts of the brain as particularly strong during wakefulness, while connections between temporal and frontal cortices are increased in strength during N1 and N2 sleep. SWS is characterised by a global drop in FC. In Chapter 3 I take advantage of rare MEG recordings of NREM sleep to show, for the first time, the feasibility of constructing source-space FC networks of sleep using power envelope correlations. The increased temporal information of MEG signals allows me to identify the specific frequencies underlying the FC differences identified in Chapter 2 with fMRI. The beta band (16 â 30 Hz) thus stands out as important for the strong posterior connectivity of wakefulness, while a range of frequency bands from delta (0.25 â 4 Hz) to sigma (13 â 16 Hz) all appear to contribute to N2-specific FC increases. Consistent with the fMRI results, slow-wave sleep shows the lowest level of FC. Interestingly, however, the MEG signals suggest a fronto-temporal network of high connectivity in the alpha band, possibly reflecting memory processes. In Chapter 4 I expand the within-frequency FC analysis of Chapter 3 to explore potential cross-frequency interactions in the MEG FC networks. It is shown that N2 sleep involves an abundance of frequency cross-talk, while SWS includes very little. A multi-layer network approach shows that the gamma band (30 â 48 Hz) is particularly integrated in wakefulness. Chapter 5 addresses the identified MEG FC findings from the perspective of traditional spectral sleep staging. By correlating temporal changes in spectral power at the sensor level to fluctuations in average FC, a specific type of transient events is found to underlie the strong N2-specific coupling in static FC values. Lastly, in Chapter 6 I make the leap out of the constraints of traditional low-resolution sleep staging, and extract dynamic…

Subjects/Keywords: 612.8; Brain activity; Sleep; Functional connectivity; Functional Magnetic Resonance Imaging; Brain state transitions; NREM sleep; Magnetoencephalography; Structural connectivity; Brain networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Stevner, A. B. A. (2017). Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:3ef218c0-a734-4d6f-abf8-ffdb780525aa ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748743

Chicago Manual of Style (16^th Edition):

Stevner, Angus Bror Andersen. “Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep.” 2017. Doctoral Dissertation, University of Oxford. Accessed January 19, 2021. http://ora.ox.ac.uk/objects/uuid:3ef218c0-a734-4d6f-abf8-ffdb780525aa ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748743.

MLA Handbook (7^th Edition):

Stevner, Angus Bror Andersen. “Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep.” 2017. Web. 19 Jan 2021.

Vancouver:

Stevner ABA. Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2021 Jan 19]. Available from: http://ora.ox.ac.uk/objects/uuid:3ef218c0-a734-4d6f-abf8-ffdb780525aa ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748743.

Council of Science Editors:

Stevner ABA. Whole-brain spatiotemporal characteristics of functional connectivity in transitions between wakefulness and sleep. [Doctoral Dissertation]. University of Oxford; 2017. Available from: http://ora.ox.ac.uk/objects/uuid:3ef218c0-a734-4d6f-abf8-ffdb780525aa ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748743

University of Melbourne

15. Pua, Emmanuel Peng Kiat. Quantifying variation in brain structure and function in autism spectrum disorders (ASD).

Degree: 2019, University of Melbourne

URL: http://hdl.handle.net/11343/224143

► Neurodevelopmental abnormalities in autism spectrum disorders (ASD) are not well defined. In particular, the link between altered neurodevelopment and ASD symptomatology remains poorly characterised. A… (more)

▼ Neurodevelopmental abnormalities in autism spectrum disorders (ASD) are not well defined. In particular, the link between altered neurodevelopment and ASD symptomatology remains poorly characterised. A key challenge is the high degree of heterogeneity in the phenotypic expression of ASD. Consequently, neuroimaging findings across multiple studies demonstrate poor generalisability and often fail to replicate. Emerging evidence suggests that heterogeneity in ASD is related to subject-specific variation. Such patterns of individualised alterations in ASD within the population could explain the inconsistency of previous findings. In the present thesis, it is hypothesised that individual differences in brain structure and function would predict individual variation in ASD symptom severity. A novel subject-level distance-based method was used to quantify individual variation in subject-specific attributes of neurodevelopment and severity of social dysfunction. The first study on brain structural morphometry from magnetic resonance imaging (MRI) investigated this hypothesis in 200 individuals with ASD and controls in a large multi-centre cohort of singletons, strictly matched at the subject-level on key confound variables. Individual differences in 19 cortical thickness and 10 surface area features selected by machine learning demonstrated out-of-sample prediction of ASD symptom severity variation. As expected, conventional group-averaged approaches showed poor prediction performance by comparison. In the second study, the subject-level distance-based method was applied to a different imaging modality of functional connectivity data from task-free functional MRI (fMRI) in the same subjects. Intrinsic brain network components were extracted with an unsupervised machine learning method for network decomposition. Individual differences in the strength of an intrinsic subnetwork predicted individual differences in social impairment severity. The subnetwork comprised of hubs of the salience network and the occipital-temporal face perception network. In the third study, we replicated and validated findings from both studies on brain structural morphometry and intrinsic connectivity in an independent monozygotic twin cohort of six locally recruited twin pairs concordant or discordant for ASD. For structural morphometry, surface area but not cortical thickness of regional features identified in the singleton cohort significantly predicted symptom severity variation in the independent twin validation cohort. For functional connectivity, the same salience and face-perception subnetwork was reproducible as a predictor of individual differences in severity of social deficits. Together, these empirical findings provide strong evidence implicating the salience network and occipital-parietal-temporal regions underlying individual differences in social dysfunction in ASD. The identified networks and cortical regions subserve typical functions of salience detection and face perception that are highly consistent with symptom domains of ASD.…

Subjects/Keywords: autism; MRI; fMRI; brain; functional connectivity; brain networks; brain anatomy; machine learning

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APA (6^th Edition):

Pua, E. P. K. (2019). Quantifying variation in brain structure and function in autism spectrum disorders (ASD). (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/224143

Chicago Manual of Style (16^th Edition):

Pua, Emmanuel Peng Kiat. “Quantifying variation in brain structure and function in autism spectrum disorders (ASD).” 2019. Doctoral Dissertation, University of Melbourne. Accessed January 19, 2021. http://hdl.handle.net/11343/224143.

MLA Handbook (7^th Edition):

Pua, Emmanuel Peng Kiat. “Quantifying variation in brain structure and function in autism spectrum disorders (ASD).” 2019. Web. 19 Jan 2021.

Vancouver:

Pua EPK. Quantifying variation in brain structure and function in autism spectrum disorders (ASD). [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/11343/224143.

Council of Science Editors:

Pua EPK. Quantifying variation in brain structure and function in autism spectrum disorders (ASD). [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/224143

Vanderbilt University

16. Petersen, Kalen John. Novel Assays of Brain Networks and Applications to Neurodegeneration.

Degree: PhD, Chemical and Physical Biology, 2019, Vanderbilt University

URL: http://hdl.handle.net/1803/12253

► The human brain is comprised of spatially separable, functionally discrete networks which can be mapped in vivo using multimodal magnetic resonance imaging (MRI). MRI permits… (more)

▼ The human brain is comprised of spatially separable, functionally discrete networks which can be mapped in vivo using multimodal magnetic resonance imaging (MRI). MRI permits interrogation of both the neuroanatomy of healthy brain circuits and identification of neuropathological dysfunction. The dentato-rubro-thalamic tract, the major efferent cerebellar white matter pathway, has an ipsilateral branch of unknown function. Tractographic analysis demonstrates that this branch is characterized by a more medial-posterior connectivity profile in the thalamus than the classical pathway, implying partial divergence. Resting-state functional connectivity reveals bilateral correlation between activity in the cerebellar dentate and thalamus, suggesting a functional role for ipsilateral connections. This network is degenerative in neurological disorders of movement and cognition such as progressive supranuclear palsy. While cortico-cerebellar loops mediate numerous motor and non-motor processes, frontal networks are critical for behavioral self-regulation. The limbic network is characterized by feedback projections involving the ventral striatum and cortical regions including the orbitofrontal cortex and anterior cingulate gyrus, critical regulators of reward-motivated activity. The dopamine-sensitive ventral striatum is functionally altered by administration of dopamine agonists as pharmacological therapy for Parkinson’s disease. Resting-state functional connectivity is elevated in mesocorticolimbic networks centered on the ventral striatum in patients who develop aberrant impulsive and compulsive behaviors as a side effect of dopamine replacement therapy. This altered pattern is associated with enhanced reward-learning proficiency. However, portions of the limbic network have poor signal in traditional functional MRI due to susceptibility and distortion in the ventral frontal lobe. As an alternative modality, perfusion-weighted arterial spin labeling (ASL) can recapitulate brain networks generally identified with the blood oxygenation level-dependent (BOLD) signal. However, optimized image pre-processing is necessary for consistent connectivity mapping. Surround subtraction, component-based noise correction, and spatial smoothing improve matching of ASL- and BOLD-derived networks. ASL-based functional connectivity is applied to the organization of the orbitofrontal cortex, and a previously hypothesized medial-lateral division is confirmed. Medial and lateral sub-regions are shown to have different functional and structural connectivity profiles; the latter is highly connected with the salience network and thus a potential mediator of impulsivity. Advisors/Committee Members: Victoria L. Morgan (committee member), Kevin D. Niswender (committee member), Daniel O. Claassen (committee member), Manus J. Donahue (committee member), Seth A. Smith (Committee Chair).

Subjects/Keywords: orbitofrontal cortex; tractography; functional connectivity; Parkinsons disease; impulsivity; brain connectivity; magnetic resonance imaging

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APA (6^th Edition):

Petersen, K. J. (2019). Novel Assays of Brain Networks and Applications to Neurodegeneration. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12253

Chicago Manual of Style (16^th Edition):

Petersen, Kalen John. “Novel Assays of Brain Networks and Applications to Neurodegeneration.” 2019. Doctoral Dissertation, Vanderbilt University. Accessed January 19, 2021. http://hdl.handle.net/1803/12253.

MLA Handbook (7^th Edition):

Petersen, Kalen John. “Novel Assays of Brain Networks and Applications to Neurodegeneration.” 2019. Web. 19 Jan 2021.

Vancouver:

Petersen KJ. Novel Assays of Brain Networks and Applications to Neurodegeneration. [Internet] [Doctoral dissertation]. Vanderbilt University; 2019. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1803/12253.

Council of Science Editors:

Petersen KJ. Novel Assays of Brain Networks and Applications to Neurodegeneration. [Doctoral Dissertation]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/12253

17. Sotelo Munoz, Miguel Renato. Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery.

Degree: 2020, Marquette University

URL: https://epublications.marquette.edu/dissertations_mu/926

► In this dissertation project, we demonstrated that diffusion magnetic resonance imaging and measures of indirect structural brain connectivity are sensitive to changes in fiber integrity… (more)

▼ In this dissertation project, we demonstrated that diffusion magnetic resonance imaging and measures of indirect structural brain connectivity are sensitive to changes in fiber integrity and connectivity to remote regions in the brain after stroke. Our results revealed new insights into the effects local lesions have on global connectivity—in particular, the cerebellum—and how these changes in connectivity and integrity relate to motor impairment. We tested this methodology on two stroke groups—subacute and chronic—and were able to show that indirect connectivity is sensitive to differences in connectivity during stroke recovery. Our work can inform clinical methods for rehabilitating motor function in stroke individuals. By introducing methodology that extends local damage to remotely connected motor related areas, we can measure Wallerian degeneration in addition to providing the framework to predict improvements in motor impairment score based on structural connectivity at the subacute stage.We used diffusion magnetic resonance imaging (dMRI), probabilistic tractography, and novel graph theory metrics to quantify structural connectivity and integrity after stroke. In the first aim, we improved on a measure of indirect structural connectivity in order to detect remote gray matter regions with reduced connectivity after stroke. In a region-level analysis, we found that indirect connectivity was more sensitive to remote changes in connectivity after stroke than measures of direct connectivity, in particular in cortical, subcortical, and cerebellar gray matter regions that play a central role in sensorimotor function. Adding this information to the integrity of the corticospinal tract (CST) improved our ability to predict motor impairment. In the second aim, we investigated the relationship between white matter integrity, connectivity, and motor impairment by developing a unified measure of white matter structure that extends local changes in white matter integrity along remotely connected fiber tracks. Our measure uniquely identified damaged fiber tracks outside the CST, correlated with motor impairment in the CST better than the FA, and also was able to relate white matter structure in the superior cerebellar peduncle to motor impairment. Our final aim used a novel connectome similarity metric and the measure of indirect structural connectivity in order to identify cross-sectional differences in white matter structure between subacute and chronic stroke. We found more reductions in indirect connectivity in the chronic stroke cerebellar fibers than the subacute group, Additionally, the indirect connectivity of the superior cerebellar peduncle at the subacute stage correlated with the improvement in motor impairment score for the paired participants. In conclusion, indirect connectivity is an important measure of global brain damage and motor impairment after stroke, and can be a useful metric to relate to brain function and stroke recovery. Advisors/Committee Members: Schmit, Brian D., Hyngstrom, Allison S., Beardsley, Scott A..

Subjects/Keywords: Brain Connectivity; Indirect Connectivity; Motor Impairment; Network; Stroke; Tractography; Biomedical Engineering and Bioengineering

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APA (6^th Edition):

Sotelo Munoz, M. R. (2020). Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery. (Thesis). Marquette University. Retrieved from https://epublications.marquette.edu/dissertations_mu/926

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sotelo Munoz, Miguel Renato. “Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery.” 2020. Thesis, Marquette University. Accessed January 19, 2021. https://epublications.marquette.edu/dissertations_mu/926.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sotelo Munoz, Miguel Renato. “Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery.” 2020. Web. 19 Jan 2021.

Vancouver:

Sotelo Munoz MR. Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery. [Internet] [Thesis]. Marquette University; 2020. [cited 2021 Jan 19]. Available from: https://epublications.marquette.edu/dissertations_mu/926.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sotelo Munoz MR. Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery. [Thesis]. Marquette University; 2020. Available from: https://epublications.marquette.edu/dissertations_mu/926

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Iraji, Armin. Brain Connectivity After Concussion.

Degree: PhD, Biomedical Engineering, 2017, Wayne State University

URL: https://digitalcommons.wayne.edu/oa_dissertations/1711

► Mild traumatic brain injury (mTBI) accounts for over one million emergency visits in the United States each year. While most mTBI patients have normal… (more)

Subjects/Keywords: Brain Connectivity; Brain Injury; Concussion; Connectivity domain; Diffusion MRI; Resting-State fMRI; Biomedical Engineering and Bioengineering; Neurosciences

11 more images…

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APA (6^th Edition):

Iraji, A. (2017). Brain Connectivity After Concussion. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/1711

Chicago Manual of Style (16^th Edition):

Iraji, Armin. “Brain Connectivity After Concussion.” 2017. Doctoral Dissertation, Wayne State University. Accessed January 19, 2021. https://digitalcommons.wayne.edu/oa_dissertations/1711.

MLA Handbook (7^th Edition):

Iraji, Armin. “Brain Connectivity After Concussion.” 2017. Web. 19 Jan 2021.

Vancouver:

Iraji A. Brain Connectivity After Concussion. [Internet] [Doctoral dissertation]. Wayne State University; 2017. [cited 2021 Jan 19]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1711.

Council of Science Editors:

Iraji A. Brain Connectivity After Concussion. [Doctoral Dissertation]. Wayne State University; 2017. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1711

19. Kabbara, Aya. Estimation des réseaux cérébraux à partir de l’EEG-hr : application sur les maladies neurologiques : Brain network estimation from dense EEG signals : application to neurological disorders.

Degree: Docteur es, Signal, Image, Vision, 2018, Rennes 1; Université libanaise

URL: http://www.theses.fr/2018REN1S028

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Le cerveau humain est un réseau très complexe. Le fonctionnement cérébral ne résulte donc pas de l'activation de régions cérébrales isolées mais au contraire met… (more)

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Le cerveau humain est un réseau très complexe. Le fonctionnement cérébral ne résulte donc pas de l'activation de régions cérébrales isolées mais au contraire met en jeu des réseaux distribués dans le cerveau (Bassett and Sporns, 2017; McIntosh, 2000). Par conséquent, l'analyse de la connectivité cérébrale à partir des données de neuroimagerie occupe aujourd'hui une place centrale dans la compréhension des fonctions cognitives (Sporns, 2010). Grâce à son excellente résolution spatiale, l'IRMf est devenue l'une des méthodes non invasives les plus couramment utilisées pour étudier cette connectivité. Cependant, l'IRMf a une faible résolution temporelle ce qui rend très difficile le suivi de la dynamique des réseaux cérébraux. Un défi considérable en neuroscience cognitive est donc l'identification et le suivi des réseaux cérébraux sur des durées courtes (Hutchison et al., 2013), généralement <1s pour une tâche de dénomination d'images, par exemple. Jusqu'à présent, peu d'études ont abordé cette question qui nécessite l'utilisation de techniques ayant une résolution temporelle très élevée (de l'ordre de la ms), ce qui est le cas pour la magnéto- ou l'électro-encéphalographie (MEG ou EEG). Cependant, l'interprétation des mesures de connectivité à partir d'enregistrements effectués au niveau des électrodes (scalp) n'est pas simple, car ces enregistrements ont une faible résolution spatiale et leur précision est altérée par les effets de conduction par le volume (Schoffelen and Gross, 2009). Ainsi, au cours des dernières années, l'analyse de la connectivité fonctionnelle au niveau des sources corticales reconstruites à partir des signaux du scalp a fait l'objet d'un intérêt croissant. L'avantage de cette méthode est d'améliorer la résolution spatiale, tout en conservant l'excellente résolution temporelle de l'EEG ou de la MEG (Hassan et al., 2014; Hassan and Wendling, 2018; Schoffelen and Gross, 2009). Cependant, l'aspect dynamique n'a pas été suffisamment exploité par cette méthode. Le premier objectif de cette thèse est de montrer comment l'approche « EEG connectivité source » permet de suivre la dynamique spatio-temporelle des réseaux cérébraux impliqués soit dans une tache cognitive, soit à l'état de repos. Par ailleurs, les études récentes ont montré que les désordres neurologiques sont le plus souvent associés à des anomalies dans la connectivité cérébrale qui entraînent des altérations dans des réseaux cérébraux «large-échelle» impliquant des régions distantes (Fornito and Bullmore, 2014). C'est particulièrement le cas pour l'épilepsie et les maladies neurodégénératives (Alzheimer, Parkinson) qui constituent, selon l'OMS, un enjeu majeur de santé publique. Dans ce contexte, la demande clinique est très forte pour de nouvelles méthodes capables d'identifier des réseaux pathologiques, méthodes simples à mettre en œuvre et surtout non invasives. Ceci est le deuxième objectif de cette thèse.

The human brain is a very complex network. Cerebral function therefore does not imply activation of isolated brain regions but…

Advisors/Committee Members: Wendling, Fabrice (thesis director), Khalil, Mohamad (thesis director).

Subjects/Keywords: Réseaux cérébraux; Connectivité fonctionnelle dynamique; Maladies neurologiques; EEG source connectivité; Brain networks; Dynamic functional connectivity; Brain disorders; EEG source connectivity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kabbara, A. (2018). Estimation des réseaux cérébraux à partir de l’EEG-hr : application sur les maladies neurologiques : Brain network estimation from dense EEG signals : application to neurological disorders. (Doctoral Dissertation). Rennes 1; Université libanaise. Retrieved from http://www.theses.fr/2018REN1S028

Chicago Manual of Style (16^th Edition):

Kabbara, Aya. “Estimation des réseaux cérébraux à partir de l’EEG-hr : application sur les maladies neurologiques : Brain network estimation from dense EEG signals : application to neurological disorders.” 2018. Doctoral Dissertation, Rennes 1; Université libanaise. Accessed January 19, 2021. http://www.theses.fr/2018REN1S028.

MLA Handbook (7^th Edition):

Kabbara, Aya. “Estimation des réseaux cérébraux à partir de l’EEG-hr : application sur les maladies neurologiques : Brain network estimation from dense EEG signals : application to neurological disorders.” 2018. Web. 19 Jan 2021.

Vancouver:

Kabbara A. Estimation des réseaux cérébraux à partir de l’EEG-hr : application sur les maladies neurologiques : Brain network estimation from dense EEG signals : application to neurological disorders. [Internet] [Doctoral dissertation]. Rennes 1; Université libanaise; 2018. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2018REN1S028.

Council of Science Editors:

Kabbara A. Estimation des réseaux cérébraux à partir de l’EEG-hr : application sur les maladies neurologiques : Brain network estimation from dense EEG signals : application to neurological disorders. [Doctoral Dissertation]. Rennes 1; Université libanaise; 2018. Available from: http://www.theses.fr/2018REN1S028

University of Toronto

20. Good, Tyler J. Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain.

Degree: 2015, University of Toronto

URL: http://hdl.handle.net/1807/70396

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Repeated sub-concussive impacts (blows transmitting force to head, but not causing overt concussion symptoms) have been linked to negative outcomes later in life, such as… (more)

Subjects/Keywords: brain connectivity; brain networks; computational modeling; Repetitive sub-concussive impacts; The Virtual Brain; whole-brain model; 0317

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Good, T. J. (2015). Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/70396

Chicago Manual of Style (16^th Edition):

Good, Tyler J. “Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain.” 2015. Masters Thesis, University of Toronto. Accessed January 19, 2021. http://hdl.handle.net/1807/70396.

MLA Handbook (7^th Edition):

Good, Tyler J. “Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain.” 2015. Web. 19 Jan 2021.

Vancouver:

Good TJ. Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain. [Internet] [Masters thesis]. University of Toronto; 2015. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1807/70396.

Council of Science Editors:

Good TJ. Modeling the Effect of Repeated Sub-concussive Impacts in Soccer Players Using The Virtual Brain. [Masters Thesis]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/70396

Stellenbosch University

21. Doruyter, Alexander Govert George. Social anxiety disorder : Functional neuroimaging and social cognitive features.

Degree: PhD, Medical Imaging and Clinical Oncology, 2018, Stellenbosch University

URL: http://hdl.handle.net/10019.1/103504

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ENGLISH SUMMARY : Neuroimaging has enabled important progress in understanding the neurobiology of social anxiety disorder (SAD). Functional neuroimaging experiments in SAD have mostly focused… (more)

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ENGLISH SUMMARY : Neuroimaging has enabled important progress in understanding the neurobiology of social anxiety disorder (SAD). Functional neuroimaging experiments in SAD have mostly focused on regional neural activity in response to anxiety provocation or processing of emotional faces, and have found hyper-activations in limbic and paralimbic circuitry. Relatively little however, is known about resting-state conditions in SAD and how these are affected by pharmacotherapy. What is known is almost entirely based on functional magnetic resonance imaging (fMRI) techniques which, while powerful, have some important limitations. Similarly, there has been only limited work investigating the resting neural correlates of social cognitive biases in SAD; how reward processing is disrupted in the condition; and how these respective features are affected by therapy. This thesis presents the first work on SAD investigating resting functional connectivity (RFC) based on nuclear neuroimaging methods. In an experiment that analysed RFC based on single photon emission computed tomography with technetium-99m hexamethyl propylene amine oxime, it was found that RFC differences in SAD were largely consistent with a contemporary network model based on fMRI, as well as implicating disrupted connectivity of the cerebellum. Another novel finding was how pharmacotherapy in SAD increased RFC of the anterior cingulate cortex. Using graph theory and resting-state fMRI, the first evidence of reduced global efficiency and increased clustering coefficients within the theory-of-mind network in SAD as well as independent evidence of social attribution bias in the same sample were reported. In an experiment that investigated regional resting metabolism in the disorder, there was evidence of abnormality in SAD compared to controls, as well as evidence of pharmacotherapy effects, in several biologically relevant regions. These results merit further investigation. Finally, in an fMRI-based experiment on reward processing in SAD, initial results identified no evidence of disrupted processing on a monetary reward task. The findings here support a neurobiological model of SAD in which alterations in resting regional metabolism may underlie disruptions in resting brain networks that have been implicated as being important in social cognitive processing. The results also suggest that pharmacotherapy may affect resting-state conditions through compensatory effects. Finally, the provisional findings are consistent with the theory that reward deficits in SAD may be limited to the processing of social reward, and may not extend to the processing of other reward types. Future SAD research should focus on collaborative work, using pooled datasets, and place greater emphasis on molecular disruptions in neurotransmitter systems involved in the disorder.

AFRIKAANSE OPSOMMING : Breinbeelding het belangrike vooruitgang in ons begrip van die neurobiologiese onderbou van sosiale angssteuring (SAS) moontlik gemaak. Funksionele breinbeeldingseksperimente in SAS het…

Advisors/Committee Members: Warwick, James Matthew, Lochner, Christine, Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Medical Imaging and Clinical Oncology. Nuclear Medicine..

Subjects/Keywords: Social phobia; Brain – Diagnostic imaging; Brain – Neuroimaging; Social phobia – Chemotherapy; Functional connectivity; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Doruyter, A. G. G. (2018). Social anxiety disorder : Functional neuroimaging and social cognitive features. (Doctoral Dissertation). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/103504

Chicago Manual of Style (16^th Edition):

Doruyter, Alexander Govert George. “Social anxiety disorder : Functional neuroimaging and social cognitive features.” 2018. Doctoral Dissertation, Stellenbosch University. Accessed January 19, 2021. http://hdl.handle.net/10019.1/103504.

MLA Handbook (7^th Edition):

Doruyter, Alexander Govert George. “Social anxiety disorder : Functional neuroimaging and social cognitive features.” 2018. Web. 19 Jan 2021.

Vancouver:

Doruyter AGG. Social anxiety disorder : Functional neuroimaging and social cognitive features. [Internet] [Doctoral dissertation]. Stellenbosch University; 2018. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10019.1/103504.

Council of Science Editors:

Doruyter AGG. Social anxiety disorder : Functional neuroimaging and social cognitive features. [Doctoral Dissertation]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/103504

Boston University

22. Dong, Yue. FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty.

Degree: MS, Medical Sciences, 2019, Boston University

URL: http://hdl.handle.net/2144/36351

► Recent estimates of prevalence of Autism Spectrum Disorders (ASD) in the United States exceeds 1.4%. Identifying neural correlates can provide important insight to help refine… (more)

Subjects/Keywords: Neurosciences; Autism spectrum disorders; Brain activity; Brain connectivity; fMRI; Intolerance of uncertainty; Neural correlates

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dong, Y. (2019). FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty. (Masters Thesis). Boston University. Retrieved from http://hdl.handle.net/2144/36351

Chicago Manual of Style (16^th Edition):

Dong, Yue. “FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty.” 2019. Masters Thesis, Boston University. Accessed January 19, 2021. http://hdl.handle.net/2144/36351.

MLA Handbook (7^th Edition):

Dong, Yue. “FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty.” 2019. Web. 19 Jan 2021.

Vancouver:

Dong Y. FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty. [Internet] [Masters thesis]. Boston University; 2019. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2144/36351.

Council of Science Editors:

Dong Y. FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty. [Masters Thesis]. Boston University; 2019. Available from: http://hdl.handle.net/2144/36351

University of Georgia

23. Dai, Ruichen. Inferring brain pathways of dynamic functional connectivity by diffusion and influence modeling.

Degree: 2016, University of Georgia

URL: http://hdl.handle.net/10724/35050

► Growing research evidence from the functional brain imaging field indicates that the propagation of functional interactions provides an essential implementation of various brain functions. However,… (more)

Subjects/Keywords: Brain functional connectivity; brain functional dynamics; network inference; information propagation and diffusion.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dai, R. (2016). Inferring brain pathways of dynamic functional connectivity by diffusion and influence modeling. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/35050

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dai, Ruichen. “Inferring brain pathways of dynamic functional connectivity by diffusion and influence modeling.” 2016. Thesis, University of Georgia. Accessed January 19, 2021. http://hdl.handle.net/10724/35050.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dai, Ruichen. “Inferring brain pathways of dynamic functional connectivity by diffusion and influence modeling.” 2016. Web. 19 Jan 2021.

Vancouver:

Dai R. Inferring brain pathways of dynamic functional connectivity by diffusion and influence modeling. [Internet] [Thesis]. University of Georgia; 2016. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10724/35050.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dai R. Inferring brain pathways of dynamic functional connectivity by diffusion and influence modeling. [Thesis]. University of Georgia; 2016. Available from: http://hdl.handle.net/10724/35050

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia State University

24. Dhakal, Kiran. Brain Functional and Structural Networks Underpinning Musical Creativity.

Degree: PhD, Physics and Astronomy, 2020, Georgia State University

URL: https://scholarworks.gsu.edu/phy_astr_diss/124

► Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity.… (more)

▼ Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity. Despite previous studies on the topics of musical improvisation, brain activations, and creativity, the main questions about the neural mechanisms for musical improvisation in efforts to unlocking the mystery of human creativity remain unanswered. What are the brain regions that are activated during the improvised performances of music? How do these brain areas coordinate activity among themselves and others during such performances? Whether and how does the brain connectivity structure encapsulate such creative skills? In attempts to contribute to answering these questions, this dissertation examines the brain activity dynamics during musical improvisation, explores white matter fiber architecture in advanced jazz improvisers using functional and structural magnetic resonance imaging (MRI) techniques. A group of advanced jazz musicians underwent functional and structural magnetic resonance brain imaging. While the functional MRI (fMRI) of their brains were collected, these expert improvisers performed vocalization and imagery improvisation and pre-learned melody tasks. The activation and connectivity analysis of the fMRI data showed that musical improvisation is characterized by higher brain activity with less functional connectivity compared to pre-learned melody in the brain network consisting of the dorsolateral prefrontal cortex (dlPFC), supplementary motor area (SMA), lateral premotor cortex (lPMC), Cerebellum (Cb) and Broca’s Area (BCA). SMA received a dominant causal information flow from dlPFC during improvisation and prelearned melody tasks. The deterministic fiber tractography analysis also revealed that the underlying white matter structure and fiber pathways in advanced jazz improvisers were enhanced in advanced jazz improvisers compared to the control group of nonmusicians, specifically the dlPFC - SMA network. These results point to the notion that an expert's performance under real-time constraints is an internally directed behavior controlled primarily by a specific brain network, that has enhanced task-supportive structural connectivity. Overall, these findings suggest that a creative act of an expert is functionally controlled by a specific cortical network as in any internally directed attention and is encapsulated by the long-timescale brain structural network changes in support of the related cognitive underpinnings. Advisors/Committee Members: Dr. Mukesh Dhamala, Dr. Martin Norgaard, Dr. Brian D. Thoms, Dr. Douglas R. Gies, Dr. Vadym Apalkov.

Subjects/Keywords: neural correlates of creativity; musical improvisation; music and brain; brain connectivity; white matter fiber tracts

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dhakal, K. (2020). Brain Functional and Structural Networks Underpinning Musical Creativity. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/phy_astr_diss/124

Chicago Manual of Style (16^th Edition):

Dhakal, Kiran. “Brain Functional and Structural Networks Underpinning Musical Creativity.” 2020. Doctoral Dissertation, Georgia State University. Accessed January 19, 2021. https://scholarworks.gsu.edu/phy_astr_diss/124.

MLA Handbook (7^th Edition):

Dhakal, Kiran. “Brain Functional and Structural Networks Underpinning Musical Creativity.” 2020. Web. 19 Jan 2021.

Vancouver:

Dhakal K. Brain Functional and Structural Networks Underpinning Musical Creativity. [Internet] [Doctoral dissertation]. Georgia State University; 2020. [cited 2021 Jan 19]. Available from: https://scholarworks.gsu.edu/phy_astr_diss/124.

Council of Science Editors:

Dhakal K. Brain Functional and Structural Networks Underpinning Musical Creativity. [Doctoral Dissertation]. Georgia State University; 2020. Available from: https://scholarworks.gsu.edu/phy_astr_diss/124

University of Pennsylvania

25. Baum, Graham Leigh. Development Of Human Brain Network Architecture Underlying Executive Function.

Degree: 2019, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/3553

► The transition from late childhood to adulthood is characterized by refinements in brain structure and function that support the dynamic control of attention and goal-directed… (more)

▼ The transition from late childhood to adulthood is characterized by refinements in brain structure and function that support the dynamic control of attention and goal-directed behavior. One broad domain of cognition that undergoes particularly protracted development is executive function, which encompasses diverse cognitive processes including working memory, inhibitory control, and task switching. Delineating how white matter architecture develops to support specialized brain circuits underlying individual differences in executive function is critical for understanding sources of risk-taking behavior and mortality during adolescence. Moreover, neuropsychiatric disorders are increasingly understood as disorders of brain development, are marked by failures of executive function, and are linked to the disruption of evolving brain connectivity. Network theory provides a parsimonious framework for modeling how anatomical white matter pathways support synchronized fluctuations in neural activity. However, only sparse data exists regarding how the maturation of white matter architecture during human brain development supports coordinated fluctuations in neural activity underlying higher-order cognitive ability. To address this gap, we capitalize on multi-modal neuroimaging and cognitive phenotyping data collected as part of the Philadelphia Neurodevelopmental Cohort (PNC), a large community-based study of brain development. First, diffusion tractography methods were applied to characterize how the development of structural brain network topology supports domain-specific improvements in cognitive ability (n=882, ages 8-22 years old). Second, structural connectivity and task-based functional connectivity approaches were integrated to describe how the development of anatomical constraints on functional communication support individual differences in executive function (n=727, ages 8-23 years old). Finally, the systematic impact of head motion artifact on measures of structural connectivity were characterized (n=949, ages 8-22 years old), providing important guidelines for studying the development of structural brain network architecture. Together, this body of work expands our understanding of how developing white matter connectivity in youth supports the emergence of functionally specialized circuits underlying executive processing. As diverse types of psychopathology are increasingly linked to atypical brain maturation, these findings could collectively lead to earlier diagnosis and personalized interventions for individuals at risk for developing mental disorders.

Subjects/Keywords: Brain Connectivity; Brain Development; Connectome; Executive Function; MRI; Network; Neuroscience and Neurobiology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Baum, G. L. (2019). Development Of Human Brain Network Architecture Underlying Executive Function. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3553

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Baum, Graham Leigh. “Development Of Human Brain Network Architecture Underlying Executive Function.” 2019. Thesis, University of Pennsylvania. Accessed January 19, 2021. https://repository.upenn.edu/edissertations/3553.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Baum, Graham Leigh. “Development Of Human Brain Network Architecture Underlying Executive Function.” 2019. Web. 19 Jan 2021.

Vancouver:

Baum GL. Development Of Human Brain Network Architecture Underlying Executive Function. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2021 Jan 19]. Available from: https://repository.upenn.edu/edissertations/3553.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Baum GL. Development Of Human Brain Network Architecture Underlying Executive Function. [Thesis]. University of Pennsylvania; 2019. Available from: https://repository.upenn.edu/edissertations/3553

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universiteit Utrecht

26. Grol, M.J. Parieto-frontal circuitry in visuomotor control.

Degree: 2008, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/25818

► One of the fundamental questions in cognitive neuroscience is the question how sensory cues influence the motor system. This thesis investigates how different circuits in… (more)

Subjects/Keywords: Sociale Wetenschappen; connectivity; human brain; fMRI; conditional motor learning; visuomotor associations

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Grol, M. J. (2008). Parieto-frontal circuitry in visuomotor control. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/25818

Chicago Manual of Style (16^th Edition):

Grol, M J. “Parieto-frontal circuitry in visuomotor control.” 2008. Doctoral Dissertation, Universiteit Utrecht. Accessed January 19, 2021. http://dspace.library.uu.nl:8080/handle/1874/25818.

MLA Handbook (7^th Edition):

Grol, M J. “Parieto-frontal circuitry in visuomotor control.” 2008. Web. 19 Jan 2021.

Vancouver:

Grol MJ. Parieto-frontal circuitry in visuomotor control. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2008. [cited 2021 Jan 19]. Available from: http://dspace.library.uu.nl:8080/handle/1874/25818.

Council of Science Editors:

Grol MJ. Parieto-frontal circuitry in visuomotor control. [Doctoral Dissertation]. Universiteit Utrecht; 2008. Available from: http://dspace.library.uu.nl:8080/handle/1874/25818

Universiteit Utrecht

27. Knaap, L.J. van der. The Corpus Callosum and Brain Hemisphere Communication; How does the corpus callosum mediate interhemispheric transfer.

Degree: 2010, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/188850

► The corpus callosum is the largest white matter structure in the human brain, connecting symmetrical and asymmetrical cortical regions of the opposing cerebral hemispheres. Complete… (more)

▼ The corpus callosum is the largest white matter structure in the human brain, connecting symmetrical and asymmetrical cortical regions of the opposing cerebral hemispheres. Complete and partial callosotomies and callosal lesion studies have provided a great opportunity to further investigate the organization and connection of motor and sensory functions across hemispheres as well as cortical representations of cognitive functions and perceptual processes and the lateralization of function. It has also granted more insight into the function of the corpus callosum, namely the facilitation of communication between the cerebral hemispheres. The corpus callosum is thought to have attributed to the functional specialization of hemispheres by mediating information transfer between hemispheres, but how the corpus callosum mediates this transfer is still a topic of debate. Some pose that the corpus callosum maintains independent processing between the two hemispheres, causing a greater connectivity to increase laterality effects. Others say that the corpus callosum shares information between hemispheres and serves an excitatory function, causing greater connectivity to decrease laterality effects. These theories are further explored by reviewing recent behavioural studies and morphological findings to tell us more about callosal function. Additional information regarding callosal function in relation to altered morphology and dysfunction in disorders is also reviewed to supplement the knowledge of callosal involvement in interhemispheric transfer. Both the excitatory as well as the inhibitory theory seem likely candidates to describe callosal function, although evidence from recent studies favour the inhibitory model. However the corpus callosum is a complex structure consisting of distinct components which could allow for the possibility to have both an excitatory or inhibitory function that can alter according to task demands. Instead of focusing on the corpus callosum as a single structure it would be beneficial for future research to investigate the functional role of the callosal sub regions, and use better methods to determine functional connectivity when looking at interhemispheric transfer. Advisors/Committee Members: Esther van den Berg, Ineke van der Ham.

Subjects/Keywords: Corpus callosum; Interhemispheric transfer; Split brain; Lateralization; Connectivity; Morphology

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APA (6^th Edition):

Knaap, L. J. v. d. (2010). The Corpus Callosum and Brain Hemisphere Communication; How does the corpus callosum mediate interhemispheric transfer. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/188850

Chicago Manual of Style (16^th Edition):

Knaap, L J van der. “The Corpus Callosum and Brain Hemisphere Communication; How does the corpus callosum mediate interhemispheric transfer.” 2010. Masters Thesis, Universiteit Utrecht. Accessed January 19, 2021. http://dspace.library.uu.nl:8080/handle/1874/188850.

MLA Handbook (7^th Edition):

Knaap, L J van der. “The Corpus Callosum and Brain Hemisphere Communication; How does the corpus callosum mediate interhemispheric transfer.” 2010. Web. 19 Jan 2021.

Vancouver:

Knaap LJvd. The Corpus Callosum and Brain Hemisphere Communication; How does the corpus callosum mediate interhemispheric transfer. [Internet] [Masters thesis]. Universiteit Utrecht; 2010. [cited 2021 Jan 19]. Available from: http://dspace.library.uu.nl:8080/handle/1874/188850.

Council of Science Editors:

Knaap LJvd. The Corpus Callosum and Brain Hemisphere Communication; How does the corpus callosum mediate interhemispheric transfer. [Masters Thesis]. Universiteit Utrecht; 2010. Available from: http://dspace.library.uu.nl:8080/handle/1874/188850

28. Bos, D.J. Under Construction : Brain connectivity and fatty acid treatment in developmental disorders.

Degree: 2016, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/326438

► Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are two of the most commonly diagnosed neurodevelopmental disorders. It is hypothesized that deficits in the development… (more)

▼ Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are two of the most commonly diagnosed neurodevelopmental disorders. It is hypothesized that deficits in the development of structural and functional connectivity underlie the psychopathology in ASD and ADHD. Further, interest in non-pharmacological treatment of psychiatric disorders has been increasing. One particular alternative that is currently being investigated is supplementation with omega-3 polyunsaturated fatty acids (PUFA’s). Using various neuroimaging methods, the studies in part I of this thesis tried to unravel the characteristics of the (dis)connected brain from multiple perspectives in both ASD and ADHD. In part II we explored the effects of omega-3 fatty acids on human brain development, and whether dietary supplementation with omega-3 fatty acids proves to be a suitable (augmentation to) therapy for ADHD by means of a double-blind randomized, placebo-controlled trial. We showed ASD is characterized by a heterogeneous pattern of changes in functional connectivity during task and rest. Further, we found with age-related differences in structural connectivity of the Forceps Minor that were related to the pattern of prefrontal gyrification in ASD. Taken together, these findings, combined with current literature, suggest that the complex pattern of increases and decreases in brain connectivity is not sufficiently captured by the current neurobiological models of ASD. This emphasizes the need for the development of more refined neurobiological models that better describe the relation between the symptoms of ASD and the underlying neurobiology. In ADHD, the increased connectivity in prefrontal resting-state networks is in keeping with a pattern of delayed network development, but these differences were present without changes in structural connectivity. Here, longitudinal studies are needed to validate whether these findings indeed fit with the hypothesized developmental delay and to elucidate how the developmental pathways of structural and functional connectivity interact. The studies in part II showed that the effect of omega-3 PUFA’s varies throughout the lifespan. During gestation and early development, omega-3 PUFAs may optimize conditions for adequate development of brain connectivity, whereas during aging omega-3 PUFAs primarily seem to have a neuroprotective effect. Further, there is some evidence to suggest that deficits in the prenatal accrual of omega-3 PUFAs may give rise to various psychiatric disorders, such as schizophrenia, depression or ADHD. However, there is no consistent evidence to date of effects of omega-3 PUFAs on neurobiology of such disorders. Specifically, our study showed that symptoms of inattention were reduced in children receiving a high dose of omega-3 PUFAs as compared to children receiving placebo. The severity of inattention problems was also related to omega-3 PUFA status as measured in cheek-cell phospholipids. However, no treatment effects were observed on cognitive control,… Advisors/Committee Members: Durston, S., Rombouts, S.A.R.B..

Subjects/Keywords: ADHD; Autism; Development; Psychiatry; Brain; Connectivity; Omega-3

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APA (6^th Edition):

Bos, D. J. (2016). Under Construction : Brain connectivity and fatty acid treatment in developmental disorders. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/326438

Chicago Manual of Style (16^th Edition):

Bos, D J. “Under Construction : Brain connectivity and fatty acid treatment in developmental disorders.” 2016. Doctoral Dissertation, Universiteit Utrecht. Accessed January 19, 2021. http://dspace.library.uu.nl:8080/handle/1874/326438.

MLA Handbook (7^th Edition):

Bos, D J. “Under Construction : Brain connectivity and fatty acid treatment in developmental disorders.” 2016. Web. 19 Jan 2021.

Vancouver:

Bos DJ. Under Construction : Brain connectivity and fatty acid treatment in developmental disorders. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2016. [cited 2021 Jan 19]. Available from: http://dspace.library.uu.nl:8080/handle/1874/326438.

Council of Science Editors:

Bos DJ. Under Construction : Brain connectivity and fatty acid treatment in developmental disorders. [Doctoral Dissertation]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/326438

29. Tax, C.M.W. Less Confusion in Diffusion MRI.

Degree: 2016, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/337377

► With its unique ability to investigate tissue architecture and microstructure in vivo, diffusion MRI (dMRI) has gained tremendous interest and the society has been continuously… (more)

▼ With its unique ability to investigate tissue architecture and microstructure in vivo, diffusion MRI (dMRI) has gained tremendous interest and the society has been continuously triggered to develop novel dMRI image analysis approaches. With the overwhelming amount of strategies currently available it is unfortunately not always evident to the end-users how dMRI can be optimally used to address their application. In addition, differences in processing strategies lead to ambiguities as to which conclusions can reliably be drawn from dMRI data, resulting in controversies in the field. Such issues hamper a smooth transition of dMRI processing strategies into useful tools for applications. This thesis contributes to reducing the confusion in diffusion MRI by scrutinizing different steps of the processing pipeline. It focuses on making the topics accessible for a broad audience, and new methodology is proposed to make more intuitive and data-driven choices in dMRI data processing, to facilitate interpretation and visualization of dMRI data, and to investigate fundamental topics such as variability in characteristics of the dMRI signal and the geometrical organization of the brain pathways. Chapter 2 introduces the different steps of the dMRI processing pipeline and reviews the most commonly used and state-of-the-art dMRI processing techniques with a focus on the brain. Chapter 3 describes the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset containing multi-modal MR data and 8000 dMRI volumes of a single healthy subject. Subsets of the MASSIVE dataset can serve as representative test beds for the development of new dMRI processing techniques. In Chapter 4 a robust parameter estimation procedure is proposed coined REKINDLE (Robust Extraction of Kurtosis INDices with Linear Estimation). By means of fast reweighted linear estimation of the diffusion kurtosis model, REKINDLE aims to identify and exclude outliers. Chapter 5 describes a data-driven framework that recursively finds single fiber population (SFP) voxels to calibrate the response function for spherical deconvolution, aiming at improved estimation of the fiber orientation distribution function. In Chapter 6, the recursive framework proposed in Chapter 5 is used to localize and characterize SFPs in multiple subjects and tracts. Chapters 7 and 8 focus on a recent debate on the existence of ‘sheet structures’ in the brain. It was proposed that pathways consistently cross each other orthogonally on surfaces somewhere along their trajectory. Others stated that these sheet structures are likely artifacts mainly based on qualitative findings. In Chapter 7, condition for sheet structure is recapitulated and a method to quantify a sheet probability index (SPI) from the data is proposed. Whereas the method in Chapter 7 requires the reconstruction of many pathways with tractography, Chapter 8 proposes a different method to calculate the SPI that does not rely on tractography and is less computationally intensive.… Advisors/Committee Members: Viergever, M.A., Leemans, A.L.G..

Subjects/Keywords: diffusion magnetic resonance imaging; medical image analysis; brain; structural connectivity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tax, C. M. W. (2016). Less Confusion in Diffusion MRI. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/337377

Chicago Manual of Style (16^th Edition):

Tax, C M W. “Less Confusion in Diffusion MRI.” 2016. Doctoral Dissertation, Universiteit Utrecht. Accessed January 19, 2021. http://dspace.library.uu.nl:8080/handle/1874/337377.

MLA Handbook (7^th Edition):

Tax, C M W. “Less Confusion in Diffusion MRI.” 2016. Web. 19 Jan 2021.

Vancouver:

Tax CMW. Less Confusion in Diffusion MRI. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2016. [cited 2021 Jan 19]. Available from: http://dspace.library.uu.nl:8080/handle/1874/337377.

Council of Science Editors:

Tax CMW. Less Confusion in Diffusion MRI. [Doctoral Dissertation]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/337377

30. Σακελλαρίου, Δημήτριος. Συνδετικότητα εγκεφάλου στο ηλεκτροεγκεφαλογράφημα ύπνου.

Degree: 2013, University of Patras

URL: http://hdl.handle.net/10889/7467

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Η συνδετικότητα εγκεφάλου αφορά σε πρότυπα δικτύων τόσο ανατομικών, όσο στατιστικά ή και αιτιακά συσχετισμένων συνδέσεων διακριτών μονάδων του νευρικού συστήματος του εγκεφάλου. Στην εργασία… (more)

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Η συνδετικότητα εγκεφάλου αφορά σε πρότυπα δικτύων τόσο ανατομικών, όσο στατιστικά ή και αιτιακά συσχετισμένων συνδέσεων διακριτών μονάδων του νευρικού συστήματος του εγκεφάλου. Στην εργασία αυτή μελετάται τόσο η απόδοση όσο και η φυσιολογική ερμηνεία μετρικών υπολογισμού της στατιστικής καθώς και αιτιακής συνάφειας χρονοσειρών. Οι χρονοσειρές αφορούν σε καταγραφές περιοχών/ηλεκτροδίων του ηλεκτροεγκεφαλογραφήματος ύπνου φυσιολογικών ανθρώπων. Πιο συγκεκριμένα, οι μέθοδοι υπολογισμού της συνδετικότητας εφαρμόζονται μεταξύ περιοχών του εγκεφάλου και σε χρονικές στιγμές όπου λαμβάνουν χώρα μικρογεγονότα του ύπνου, όπως υπνικές άτρακτοι (sleep spindles) και K-συμπλέγματα (K-complexes), με απώτερο σκοπό την κατανόηση του ρόλου αυτών των γεγονότων στον ύπνο.

During Non-Rapid Eye Movement (NREM) sleep brain is considered to be relatively disconnected from the environment. Also connectedness between brain areas has been found decreased, although we do not know the role played in this by specific elements of sleep microstructure. We developed a method with millisecond time resolution appropriate for assessing brain connectivity during NREM sleep spindles, the φ-coherence. It is based on the observation by Nolte (2008) that when the phase between two signals is zero, the coherence value can be attributed to volume conduction rather than functional neuronal connection. So φ-coherence excludes this value. The new method counts among the effective connectivity measures as advantageous in (a) its superb time resolution (b) ability to study events clustered from different time periods or subjects (c) simultaneous study of any choice from all possible combinations of EEG electrodes and display their φ-coherence in time-frequency topological maps and (d) parameterization of all the plots included in the maps regarding frequency, time and φ-coherence threshold. Preliminary results from 360 fast spindles recorded in whole night sleep of two healthy volunteers the use of φ-coherence indicated a prevailing connectivity pattern of causal interactions mostly from centroparietal regions (C3, Cz, C4, Pz, P3, P4) to right frontotemporal regions (F8, T4). The study aims to help our understanding of the role played by spindles not only in sleep maintenance but also in memory consolidation and in several neuropsychiatric disorders.

Advisors/Committee Members: Κωστόπουλος, Γεώργιος, Sakellariou, Dimitrios, Κουτρουμανίδης, Μιχάλης, Μεγαλοοικονόμου, Βασίλειος.

Subjects/Keywords: Συνδετικότητα; Ηλεκτροεγκεφαλογράφημα (ΗΕΓ); Νευροεπιστήμες; 612.821; Connectivity; Neurosciences (EEG); Brain

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Σακελλαρίου, �. (2013). Συνδετικότητα εγκεφάλου στο ηλεκτροεγκεφαλογράφημα ύπνου. (Masters Thesis). University of Patras. Retrieved from http://hdl.handle.net/10889/7467

Chicago Manual of Style (16^th Edition):

Σακελλαρίου, Δημήτριος. “Συνδετικότητα εγκεφάλου στο ηλεκτροεγκεφαλογράφημα ύπνου.” 2013. Masters Thesis, University of Patras. Accessed January 19, 2021. http://hdl.handle.net/10889/7467.

MLA Handbook (7^th Edition):

Σακελλαρίου, Δημήτριος. “Συνδετικότητα εγκεφάλου στο ηλεκτροεγκεφαλογράφημα ύπνου.” 2013. Web. 19 Jan 2021.

Vancouver:

Σακελλαρίου �. Συνδετικότητα εγκεφάλου στο ηλεκτροεγκεφαλογράφημα ύπνου. [Internet] [Masters thesis]. University of Patras; 2013. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10889/7467.

Council of Science Editors:

Σακελλαρίου �. Συνδετικότητα εγκεφάλου στο ηλεκτροεγκεφαλογράφημα ύπνου. [Masters Thesis]. University of Patras; 2013. Available from: http://hdl.handle.net/10889/7467

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