In this thesis we apply the idea of a barcode from persistent homology to four hierarchical clustering methods: single, average, complete, and Ward's linkage. Desirable theoretical properties of dendrograms, the standard tool to visualize the output of hierarchical clustering methods, were described by Carlsson. We define analogous properties for hierarchical clustering quasi-barcodes and prove that average and complete quasi-barcodes possess a property that dendrograms do not. We discuss how to decide where to "cut" the output of hierarchical clustering quasi-barcodes based on the distance between the heights at which clusters merge. We find the best possible matching for calculating the Wasserstein distance between quasi-barcodes built from the same number of data points all born at time 0. We also prove that single, average, and complete quasi-barcodes are stable in the sense that small perturbations in distances between points produce small changes in quasi-barcodes. In order to test the efficiency of quasi-barcodes and the cut-off criteria, we generate datasets of points arranged in blobs or concentric circles and look whether the combination of the quasi-barcode with the cut-off criteria successfully finds the right amount of clusters in the dataset and whether it places points in the correct clusters. Finally, we apply these tools to datasets from New York University and Peking University of typically developed controls and attention hyperactivity deficit disorder subjects between the ages of 7 and 18.

Cell count is an important metric in neurological research. The loss in numbers of certain cells like neurons has been found to accompany not only the deterioration of important brain functions but disorders like clinical depression as well. Since the manual counting of cell numbers is a near impossible task considering the sizes and numbers involved, an automated approach is the obvious alternative to arrive at the cell count. In this thesis, a software application is described that automatically segments, counts, and helps visualize the various cell bodies present in a sample mouse brain, by analyzing the images produced by the Knife-Edge Scanning Microscope (KESM) at the Brain Networks Laboratory. The process is described essentially in five stages: Image acquisition, Pre- Processing, Processing, Analysis and Refinement, and finally Visualization. Nissl staining is a staining mechanism that is used on the mouse brain sample to highlight the cell bodies of our interest present in the brain, namely neurons, granule cells and interneurons. This stained brain sample is embedded in solid plastic and imaged by the KESM, one section at a time. The volume that is digitized by this process is the data that is used for the purpose of segmentation. While most sections of the mouse brain tend to be comprised of sparsely populated neurons and red blood cells, certain sections near the cerebellum exhibit a very high density and population of smaller granule cells, which are hard to segment using simpler image segmentation techniques. The problem of the sparsely populated regions is tackled using a combination of connected component labeling and template matching, while the watershed algorithm is applied to the regions of very high density. Finally, the marching cubes algorithm is used to convert the volumetric data to a 3D polygonal representation. Barring a few initializations, the process goes ahead with minimal manual intervention. A graphical user interface is provided to the user to view the processed data in 2D or 3D. The interface offers the freedom of rotating and zooming in/out of the 3D model, as well as viewing only cells the user is interested in analyzing. The segmentation results achieved by our automated process are compared with those obtained by manual segmentation by an independent expert.

Cell count is an important metric in neurological research. The loss in numbers of certain cells like neurons has been found to accompany not only the deterioration of important brain functions but disorders like clinical depression as well. Since the manual counting of cell numbers is a near impossible task considering the sizes and numbers involved, an automated approach is the obvious alternative to arrive at the cell count. In this thesis, a software application is described that automatically segments, counts, and helps visualize the various cell bodies present in a sample mouse brain, by analyzing the images produced by the Knife-Edge Scanning Microscope (KESM) at the Brain Networks Laboratory. The process is described essentially in five stages: Image acquisition, Pre- Processing, Processing, Analysis and Refinement, and finally Visualization. Nissl staining is a staining mechanism that is used on the mouse brain sample to highlight the cell bodies of our interest present in the brain, namely neurons, granule cells and interneurons. This stained brain sample is embedded in solid plastic and imaged by the KESM, one section at a time. The volume that is digitized by this process is the data that is used for the purpose of segmentation. While most sections of the mouse brain tend to be comprised of sparsely populated neurons and red blood cells, certain sections near the cerebellum exhibit a very high density and population of smaller granule cells, which are hard to segment using simpler image segmentation techniques. The problem of the sparsely populated regions is tackled using a combination of connected component labeling and template matching, while the watershed algorithm is applied to the regions of very high density. Finally, the marching cubes algorithm is used to convert the volumetric data to a 3D polygonal representation. Barring a few initializations, the process goes ahead with minimal manual intervention. A graphical user interface is provided to the user to view the processed data in 2D or 3D. The interface offers the freedom of rotating and zooming in/out of the 3D model, as well as viewing only cells the user is interested in analyzing. The segmentation results achieved by our automated process are compared with those obtained by manual segmentation by an independent expert.

Several major functional networks in the brain have been identified, based on sub-regions in the brain that display functionally correlated, synchronous activity and perform common cognitive functions. Three such brain networks (default mode network - DMN, central executive network - CEN, and salience network - SN) form a tri-network model of higher cognitive functioning and are found to be dysregulated in a number of psychopathologies, such as PTSD, autism, schizophrenia, anxiety, depression, bipolar disorder and fronto-temporal dementia (FTD). Current therapies that improve the patient’s cognitive and behavioural states are also found to re-normalize these dysregulated networks, suggesting a correlation between network dysfunction and behavioural dysregulation. Hence, assessing tri-network activity and its dynamics can be a powerful tool to objectively assess treatment response in such psychopathologies. Doing so would most likely rely on functional magnetic resonance imaging (fMRI), as one of the most commonly used modalities for studying such brain networks. While fMRI allows for superior spatial resolution, it poses serious challenges to widespread clinical adoption due to MRI's high operational costs and poor temporal resolution of the acquired signal. One potential strategy to overcome this shortcoming is by identifying the activity of these networks using their EEG-based temporal signatures, greatly reducing the cost and increasing accessibility of using such measures. This thesis takes a step towards improving the clinical accessibility of such brain network-based biomarkers. Doing so first required the exploration of a popular EEG-based method currently being used to study brain networks in mental health disorders - Microstates. This work uncovered flaws in the core assumptions made in assessing Microstates, necessitating the development of an alternate method to detect such network activity using EEG. To accomplish this, it was important to understand the healthy dynamics between the three brain networks constituting the tri-network model and test one of the core predictions of this model, i.e. the SN gates the DMN and CEN activation based on interoceptive and exteroceptive task demands. Probing this question next uncovered mechanistic details of this process, discovering that the SN co-activates with the task-relevant network. Using this information, a novel machine learning pipeline was developed that used simultaneous EEG-fMRI data to identify EEG-based signatures of the three networks within the tri-network model, and could use these signatures to predict network activation. Finally, the novel machine learning pipeline was trialed in a study investigating the effects of lifestyle interventions on the network dynamics, showing that CEN-SN synchrony can predict response to intervention, while DMN-SN synchrony can develop in those that fail to respond. The understanding of healthy network dynamics gathered from the earlier study helps interpret these results, suggesting that the non-responders persistently activated DMN as a maladaptive strategy. In conclusion, the studies discussed in this thesis have improved our understanding of healthy network dynamics, uncovered critical flaws in currently popular methods of EEG-based network analysis, provided an alternative methodology to assess network dynamics using EEG, and also validated its use in tracking changes in network synchrony. The identified EEG signatures of widely used functional networks, will greatly increase the clinical accessibility of such brain network measures as biomarkers for neuropathologies. Monitoring the level of network activity in affected subjects may also lead to the development of novel individualized treatments such as brain network-based neurofeedback interventions. / Thesis / Doctor of Philosophy (PhD) / Synergistic activity in specific brain regions gives rise to large-scale brain networks, linked to specific cognitive tasks. Interactions between three such brain networks are believed to underlie healthy behavior and cognition, and these are found to be disrupted in those with mental health disorders. The ability to cheaply and effectively detect these networks can enable routine network-based clinical assessments, improving diagnosis of mental health disorders and tracking their response to treatment. The first study in this thesis found major flaws in a popular method to assess these networks using a suitably cheap imaging method called electroencephalography(EEG). The remainder of the thesis addressed these issues by first identifying healthy patterns of network activity, followed by designing a novel method to identify network activity using EEG. The final study validates the developed method by tracking network changes after lifestyle interventions. In sum, this thesis takes a step towards improving the clinical accessibility of such brain network-based biomarkers.

Introduction: The brain can change its structure and functionality as a result ofexternal factors. The working memory (WM) of the brain is where informationcan be held and manipulated during a short period of time, with the purpose ofachieving higher cognitive functions such as reasoning and learning. The WMimproves in capacity during the development from childhood into adulthood,and variation of improvement is possible as an effect of situational factors andstimuli.Goal: The main goal of this project was to examine the effects of a WMtraining program on power distribution, connectivity and synchronicity withinbrain networks, using an intra-individual analysis approach.Method: A series of magnetoencephalography (MEG) measurements wasacquired for four subjects while they were performing WM and control tasks,during a WM training program, along with an MRI image of the brain for eachof the participants. The data was preprocessed for noise and artifact removaland a source reconstruction was performed. Time-frequency representationsof the data were created and the frequencies were categories into alpha,beta and gamma bands. The power difference between the WM and controltask was calculated as a function of cognitive load of each frequency band,and its variation over load was calculated as a constructed metric called’area under power difference curve’ (AUPDC), and visualised using colourscale representation upon the brain MRI of each subject. Brain parcels thatsignificantly deviated from a random distribution of AUPDC values wereidentified using a Gaussian distribution fit.Results and discussion: All subjects showed a clear improvement inperformance accuracy of the tasks, but as the effect on the power distributionsvaried considerably for each subject and frequency band, other aspects besidepower need to be investigated in order to understand the mechanisms behindthe improvement. However, the overall results indicate that many significantAUPDC values seem to have decreased during the WM training, both forthe positive and negative significant AUPDC values, suggesting a strongerdecreasing trend in power difference over cognitive load and a weaker increasingtrend. This could suggest an improved brain activation efficiency as an effectof the WM training.

The study of complex brain networks, where the brain can be viewed as a system with various interacting regions that produce complex behaviors, has grown tremendously over the past decade. With both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for dynamic brain network analysis are needed. We first propose a paradigm for longitudinal brain network analysis over patient cohorts where we adapt the Stochastic Actor Oriented Model (SAOM) framework and model a subject's network over time as observations of a continuous time Markov chain. Network dynamics are represented as being driven by various factors, both endogenous (i.e., network effects) and exogenous, where the latter include mechanisms and relationships conjectured in the literature. We outline an application to the resting-state fMRI network setting, where we draw conclusions at the subject level and then perform a meta-analysis on the model output. As an extension of the models, we next propose an approach based on Hidden Markov Models to incorporate and estimate type I and type II error (i.e., of edge status) in our observed networks. Our model consists of two components: 1) the latent model, which assumes that the true networks evolve according to a Markov process as they did in the original SAOM framework; and 2) the measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for estimation. Lastly, we focus on the study of percolation - the sudden emergence of a giant connected component in a network. This has become an active area of research, with relevance in clinical neuroscience, and it is of interest to distinguish between different percolation regimes in practice. We propose a method for estimating a percolation model from a given sequence of observed networks with single edge transitions. We outline a Hidden Markov Model approach and EM algorithm for the estimation of the birth and death rates for the edges, as well as the type I and type II error rates. / 2018-07-25T00:00:00Z

Today, video communication is common in private and professional communication, and during corona pandemic 2020, its use has increased significantly. This has raised the issue on the fact that video communication is not as perceived as natural as a face-to-face conversation, and the lack of eye contact can be a contributing cause. This study has developed and evaluated a video communication design where it was possible for users to have eye contact. It was also possible to manipulate the camera position. The aim of the study was to evaluate the usefulness of the design in research on eye contact, which gave the opportunity to also investigate how this affects the experience of the conversation. The study also investigated how the self-view affects experience of conversations. Twelve persons participated in the study. After a relaxed conversation, a semi-structured interview was conducted on how they experienced the different camera angles. The participants eye movements were also recorded. The result shows a significant and consistent perceived difference between different camera positions. The usual camera position with 15° decentration felt familiar and the extreme decentration of 45° position felt unreal and abnormal. When given the opportunity for eye contact, the participants felt significantly more present in the conversation with increased sense of reality. The Self-view was perceived as an obstacle to feel present, but gave a sense of control. These results are discussed in relation to the need to adapt video communication to social processes and its biological origin, e.g. the eyes function for we-ness and the implication of seeing oneself during conversations.

Decisions often require weighing the costs and benefits of available prospects. Value-based decision making depends on the coordination of multiple cognitive faculties, making it potentially susceptible to at least two forms of variability. First, there is heterogeneity in brain organization across individuals in areas of association cortex that exhibit decision-related activity. Second, a person’s preferences can fluctuate even for repetitive decision scenarios. Using functional magnetic resonance imaging (fMRI) and behavioral experiments in humans, this project explored how these distinct sources of variability impact choice evaluation, localization of valuation in the brain, and the links between valuation and other cognitive phenomena. Group-level findings suggest that valuation processes share a neural representation with the “default network” (DN) in medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). Study 1 examined brain network variability in an open dataset of resting-state fMRI (n=100) by quantitatively testing the hypothesis that the spatial layout of the DN is unique to each person. Functional network topography was well-aligned across individuals in PCC, but highly idiosyncratic in mPFC. These results highlighted that the apparent overlap of cognitive functions in these areas should be evaluated within individuals. Study 2 examined variability in the integration of rewards with subjective costs of time and effort. Two computerized behavioral experiments (total n=132) tested how accept-or-reject foraging decisions were influenced by demands for physical effort, cognitive effort, and unfilled delay. The results showed that people’s willingness to incur the three types of costs differed when they experienced a single type of demand, but gradually converged when all three were interleaved. The results could be accounted for by a computational model in which contextual factors altered the perceived cost of temporal delay. Finally, Study 3 asked whether the apparent cortical overlap between valuation effects and the DN persisted after accounting for individual variability in brain topography and behavior. Using fMRI scans designed to evoke valuation and DN-like effects (n=18), we reproduced the idiosyncratic network topography from Study 1, and observed valuation-related effects in individually identified DN regions. Collectively, these findings advance our taxonomic understanding of higher-order cognitive processes, suggesting that seemingly dissimilar valuation and DN-related functions engage overlapping cortical mechanisms.

Variability in the brain’s functional network connectivity is associated with differences in cognition. The degree to which brain networks flexibly reconfigure, or alternatively remain stable, can differ across regions of cortex, across time, and across individuals. The goal of this dissertation was to investigate how the brain’s functional network architecture is reconfigured to support abstract reasoning and rule learning. I proposed that flexibility within frontoparietal cortex, combined with a stable network core, is beneficial for effective reasoning and rule learning. Experiment One investigated the activation patterns and dynamic community structure of brain networks associated with shifting task demands during abstract reasoning. Twenty-seven subjects underwent fMRI scanning during resting state and during a subsequent abstract reasoning task. When quantifying network reconfiguration between resting and task states, I found a stable system within default and somatomotor networks alongside a more flexible frontoparietal control network. The results motivated a novel understanding of how the brain performs reasoning tasks: an underlying stable functional network acts as a cognitive control mechanism, priming task-active nodes within frontoparietal cortex to variably activate for unique task conditions. Experiment Two used a dynamic network analysis to identify changes in functional brain networks that were associated with context-dependent rule learning. During fMRI scanning, twenty-nine naïve subjects were challenged to learn a set of context-dependent rules. Successful learners showed greater stability in ventral attention and somatomotor regions, increased assortative mixing of cognitive control regions as rules were learned, and greater segregation of attention networks throughout the entire task. The results suggested that a stable ventral attention network and a flexible frontoparietal control network support sustained attention and the formation of rule representations. In Experiment Three, I carried out a separate analysis of data from Experiment 2 to characterize the functional connectivity patterns with the hippocampus that emerged during successful rule learning. The results demonstrated that the hippocampal head became increasingly functionally connected to the lateral frontal pole and caudate in successful learners. Additionally, the entire hippocampus exhibited decreased functional connectivity with the mid-cingulate and precuneus in successful learners. These three experiments demonstrated that stable functional connectivity in somatomotor and ventral attention networks, combined with flexible reconfiguration of frontoparietal cortex, is advantageous for successful rule learning and abstract reasoning. Altogether, this dissertation demonstrated that individual differences in dynamic functional connectivity are associated with learning, and that stability of brain networks across time and tasks supports higher order cognition. / 2025-01-23T00:00:00Z

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