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Open Access Publications from the University of California

Scholarly Works (48 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2013)

Whole-brain structural and functional connectivity networks can be assessed using diffusion-weighted MRI (DW-MRI) and functional MRI (fMRI), respectively. When the brain is parcellated into its constituent subregions, specific methods quantify the relative connectivity strengths between pairs of regions. The calculation of connectivity between all pairs of regions produces a connectivity matrix. With such a matrix, mathematical methods from graph theory characterize the network for global properties of integration, segregation, and robustness. At the regional level, these methods quantify specific properties such as connection density, convergence, and isolation. In this work, we apply these methods to understand how the possession of the Apolipoprotein E ε4 allele, the primary genetic risk factor for late-onset Alzheimer's Disease (AD), contributes to global and local alterations of structural and functional connectivity. In Chapter 2, we find that DW-MRI-based fiber tractography networks in aging APOE-4 carriers exhibit accelerated negative correlations between age and clustering coefficient, a measure of local axonal connection density. This trend occurs simultaneously with reductions in global cortical thickness and decrease performance on episodic memory tests. In a highly similar population, we examined fMRI-based functional connectivity networks during performance on an episodic memory task (Chapter 3). APOE-4 carriers demonstrated reduced activation during memory encoding in the entorhinal cortex, a locus of early disease change in Alzheimer's disease. The degree of activation in this region correlated with the amount of functional brain integration, suggesting a global basis for local alterations in neuronal activity. In Chapter 4, we assessed hippocampal functional and structural connectivity during episodic memory consolidation in healthy young adults. Results indicate that elevated functional connectivity in a hippocampal-cortical network was important for the process of consolidation. The structural connections of this network all traversed the parahippocamal gyrus, an area of known structural atrophy in individuals at genetic risk for AD. In Chapter 5 we describe a web-based tool for the public sharing and analysis of brain connectivity matrices, and then apply it to reveal substantial differences in the topology of whole brain structural and functional networks. Finally, Chapter 6 contains a model of cortico-hippocampal connectivity that unifies the findings from these studies.

    Cover page: Multimodality MRI-based Brain Network Analysis: Applications to Genetic Risk for Alzheimer's Disease
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2022)
    The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.
      Cover page: A dynamic gradient architecture generates brain activity states
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2017)
      We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views-such as heat maps, node link diagrams and anatomical views-using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g., community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies-exploring progressive supranuclear palsy, as well as memory encoding and retrieval.
        Cover page: Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2023)
        BACKGROUND: Treatment-resistant depression (TRD) refers to patients with major depressive disorder who do not remit after 2 or more antidepressant trials. TRD is common and highly debilitating, but its neurobiological basis remains poorly understood. Recent neuroimaging studies have revealed cortical connectivity gradients that dissociate primary sensorimotor areas from higher-order associative cortices. This fundamental topography determines cortical information flow and is affected by psychiatric disorders. We examined how TRD impacts gradient-based hierarchical cortical organization. METHODS: In this secondary study, we analyzed resting-state functional magnetic resonance imaging data from a mindfulness-based intervention enrolling 56 patients with TRD and 28 healthy control subjects. Using gradient extraction tools, baseline measures of cortical gradient dispersion within and between functional brain networks were derived, compared across groups, and associated with graph theoretical measures of network topology. In patients, correlation analyses were used to associate measures of cortical gradient dispersion with clinical measures of anxiety, depression, and mindfulness at baseline and following the intervention. RESULTS: Cortical gradient dispersion was reduced within major intrinsic brain networks in patients with TRD. Reduced cortical gradient dispersion correlated with increased network degree assessed through graph theory-based measures of network topology. Lower dispersion among default mode, control, and limbic network nodes related to baseline levels of trait anxiety, depression, and mindfulness. Patients baseline limbic network dispersion predicted trait anxiety scores 24 weeks after the intervention. CONCLUSIONS: Our findings provide preliminary support for widespread alterations in cortical gradient architecture in TRD, implicating a significant role for transmodal and limbic networks in mediating depression, anxiety, and lower mindfulness in patients with TRD.
          Cover page: Dysfunctional Cortical Gradient Topography in Treatment-Resistant Major Depressive Disorder.
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2012)
          Brain connectomics research has rapidly expanded using functional MRI (fMRI) and diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a connectivity matrix (CM). A CM stores the connection strength between any two regions ("nodes") in a brain network. This format is useful for several reasons: (1) it is highly distilled, with minimal data size and complexity, (2) graph theory can be applied to characterize the network's topology, and (3) it retains sufficient information to capture individual differences such as age, gender, intelligence quotient (IQ), or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI) and disorders (Alzheimer's, autism, Attention Deficit Hyperactive Disorder). To demonstrate the site's functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects' (ages 26-45) resting state fMRI (rs-fMRI) and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta-analyses comparing networks across imaging modality, age group, and disease state.
            Cover page: The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2020)
            We introduce an approach for the interactive visual analysis of weighted, dynamic networks. These networks arise in areas such as computational neuroscience, sociology, and biology. Network analysis remains challenging due to complex time-varying network behavior. For example, edges disappear/reappear, communities grow/vanish, or overall network topology changes. Our technique, TimeSum, detects the important topological changes in graph data to abstract the dynamic network and visualize one summary representation for each temporal phase, a state. We define a network state as a graph with similar topology over a specific time interval. To enable a holistic comparison of networks, we use a difference network to depict edge and community changes. We present case studies to demonstrate that our methods are effective and useful for extracting and exploring complex dynamic behavior of networks.
              Cover page: State-based network similarity visualization
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2012)
              Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.
                Cover page: Insights into multimodal imaging classification of ADHD
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2014)
                In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or topics, provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions.
                  Cover page: Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD.
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2015)
                  BACKGROUND:Intensive cognitive-behavioral therapy (CBT) can effectively reduce symptoms in obsessive-compulsive disorder (OCD). However, many relapse after treatment. Few studies have investigated biological markers predictive of follow-up clinical status. The objective was to determine if brain network connectivity patterns prior to intensive CBT predict worsening of clinical symptoms during follow-up. METHODS:We acquired resting-state functional magnetic resonance imaging data from 17 adults with OCD prior to and following 4 weeks of intensive CBT. Functional connectivity data were analyzed to yield graph-theory metrics. We examined the relationship between pre-treatment connectome properties and OCD clinical symptoms before and after treatment and during a 12-month follow-up period. RESULTS:Mean OCD symptom decrease was 40.4 ± 16.4% pre- to post-treatment (64.7% responded; 58.8% remitted), but 35.3% experienced clinically significant worsening during follow-up. From pre- to post-treatment, small-worldness and clustering coefficient significantly increased. Decreases in modularity correlated with decreases in OCD symptoms. Higher pre-treatment small-world connectivity was significantly associated with worsening of OCD symptoms during the follow-up period. Psychometric and neurocognitive measures pre- and post-treatment were not significant predictors. CONCLUSION:This is the first graph-theory connectivity study of the effects of CBT in OCD, and the first to test associations with follow-up clinical status. Results show functional network efficiency as a biomarker of CBT response and relapse in OCD. CBT increases network efficiency as it alleviates symptoms in most patients, but those entering therapy with already high network efficiency are at greater risk of relapse. Results have potential clinical implications for treatment selection.
                    Cover page: Brain Connectivity and Prediction of Relapse after Cognitive-Behavioral Therapy in Obsessive–Compulsive Disorder
                    • Article
                    • Peer Reviewed
                    UC San Francisco Previously Published Works (2018)

                    Introduction

                    Affective changes precede cognitive decline in mild Alzheimer's disease and may relate to increased connectivity in a "salience network" attuned to emotionally significant stimuli. The trajectory of affective changes in preclinical Alzheimer's disease, and its relationship to this network, is unknown.

                    Methods

                    One hundred one cognitively normal older adults received longitudinal assessments of affective symptoms, then amyloid-PET. We hypothesized amyloid-positive individuals would show enhanced emotional reactivity associated with salience network connectivity. We tested whether increased global connectivity in key regions significantly related to affective changes.

                    Results

                    In participants later found to be amyloid positive, emotional reactivity increased with age, and interpersonal warmth declined in women. These individuals showed higher global connectivity within the right insula and superior temporal sulcus; higher superior temporal sulcus connectivity predicted increasing emotional reactivity and decreasing interpersonal warmth.

                    Conclusions

                    Affective changes should be considered an early preclinical feature of Alzheimer's disease. These changes may relate to higher functional connectivity in regions critical for social-emotional processing.
                      Cover page: Early affective changes and increased connectivity in preclinical Alzheimer's disease.
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