Developing and Validating Open Source Tools for Advanced Neuroimaging Research

Almost all scientific research relies on software. This is particularly true for research that uses neuroimaging technologies, such as functional magnetic resonance imaging (fMRI). These technologies generate massive amounts of data per participant, which must be processed and analyzed using specialized software. A large portion of these tools are developed by teams of researchers, rather than trained software developers. In this kind of ecosystem, where the majority of software creators are scientists, rather than trained programmers, it becomes more important than ever to rely on community-based development, which may explain why most of this software is open source. It is in the development of this kind of research-oriented, open source software that I have focused much of my graduate training, as is reflected in this dissertation. One software package I have helped to develop and maintain is tedana, a Python library for denoising multi-echo fMRI data. In chapter 2, I describe this library in a short, published software paper. Another library I maintain as the primary developer is NiMARE, a Python library for performing neuroimaging meta-analyses and derivative analyses, such as automated annotation and functional decoding. In chapter 3, I present NiMARE in a hybrid software paper with embedded tutorial code exhibiting the functionality of the library. This paper is currently hosted as a Jupyter book that combines narrative content and code snippets that can be executed online. In addition to research software development, I have focused my graduate work on performing reproducible, open fMRI research. To that end, chapter 4 is a repli- cation and extension of a recent paper on multi-echo fMRI denoising methods Power et al. (2018a). This replication was organized as a registered report, in which the introduction and methods were submitted for peer review before the analyses were performed. Finally, chapter 5 is a conclusion to the dissertation, in which I reflect on the work I have done and the skills I have developed throughout my training

Developing High-Density Diffuse Optical Tomography for Neuroimaging

Clinicians who care for brain-injured patients and premature infants desire a bedside monitor of brain function. A decade ago, there was hope that optical imaging would be able to fill this role, as it combined fMRI\u27s ability to construct cortical maps with EEG\u27s portable, cap-based systems. However, early optical systems had poor imaging performance, and the momentum for the technique slowed. In our lab, we develop diffuse optical tomography: DOT), which is a more advanced method of performing optical imaging. My research has been to pioneer the in vivo use of DOT for advanced neuroimaging by: 1) quantifying the advantages of DOT through both in silico simulation and in vivo performance metrics,: 2) restoring confidence in the technique with the first retinotopic mapping of the visual cortex: a benchmark for fMRI and PET), and: 3) creating concepts and methods for the clinical translation of DOT. Hospitalized patients are unable to perform complicated neurological tasks, which has motivated us to develop the first DOT methods for resting-state brain mapping with functional connectivity. Finally, in collaboration with neonatologists, I have extended these methods with proof-of-principle imaging of brain-injured premature infants. This work establishes DOT\u27s improvements in imaging performance and readies it for multiple clinical and research roles

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development

The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0–5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics

Quasi-periodic patterns of brain intrinsic activity coordinate the functional connections in humans

The brain is a complex self-organizing biophysical system and intrinsically very active. How such intrinsic activity organizes the brain in humans is widely being studied during resting-state using functional magnetic resonance imaging (rsfMRI) and the functional connectivity (FC) metric. FC, calculated as the Pearson correlation between rsfMRI timeseries from different brain areas, indicates coherent activity on average over time, and can reflect some spatial aspects of the brain’s intrinsic organization. For example, based on the FC profile of each area, the cerebral cortex can be parcellated into a few resting-state networks (RSNs) or exhibit a few functional connectivity gradients (FCGs). Brain is a complex system and exhibits varied dynamic spatiotemporal regimes of coherent activity, which are still poorly understood. A subset of such regimes should be giving rise to FC, yet they might entail significantly insightful aspects about the brain’s self-organizing processes, which cannot be captured by FC. Among such dynamic regimes is the quasi-periodic pattern (QPP), obtained by identifying and averaging similar ~20s-long segments of rsfMRI timeseries. QPP involves a cycle of activation and deactivation of different areas with different timings, such that the overall activity within QPP resembles RSNs and FCGs, suggesting QPP might be contributing to FC. To robustly detect multiple QPPs, method improvements were implemented and three primary QPPs were thoroughly characterized. Within these QPPs activity propagates along the functional gradients at the cerebral cortex and most subcortical regions, in a well-coordinated way, because of the consistencies and synchronies across all brain regions which reasonably accord with the consensus on the structural connections. Nuanced timing differences between regions and the closed flow of activity throughout the brain suggest drivers for these patterns. When three QPPs are removed from rsfMRI timeseries, FC within and particularly between RSNs remarkably reduces, illustrating their dominant contribution. Together, our results suggest a few recurring spatiotemporal patterns of intrinsic activity might be dominantly coordinating the functional connections across the whole brain and serving self-organization. These intrinsic patterns possibly interact with the external tasks, affecting performance, or might provide more sensitive biomarkers in certain disorders and diseases.Ph.D

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Neurobiological Impact of HIV Infection and Chronic Cannabis Use

Neuroimaging research has identified brain alterations linked with the human immunodeficiency virus (HIV) that contribute to cognitive declines characterizing the disease. Given cannabis’s (CB’s) anti-inflammatory properties, use prevalence among people living with HIV (PLWH), and impact on neurocognition, my dissertation utilizes a between-groups study design to interrogate separate and interactive effects of HIV and CB on fMRI measures of brain activity. We investigate (1) task-based brain activity at the regional-level, (2) insular resting-state functional connectivity (rsFC) at the circuit-level, and (3) large-scale brain network interactions at the systems-level. Participants (N=114) were stratified into four groups (HIV+/CB+; HIV+/CB-; HIV-/CB+; HIV-/CB-) and underwent fMRI scanning while completing an Error Awareness Task (EAT) and while at rest. Participants also completed a battery of instruments including subjective reports of cognitive failures, and objective measures of cognition and medication management abilities. Blood samples quantified disease severity (viral load) and inflammation (tumor necrosis factor alpha [TNF-α]). Regarding task-based brain activity, PLWH displayed a lack of error-related deactivation in two default mode network (DMN) regions (posterior cingulate cortex [PCC], medial prefrontal cortex [mPFC]). Across all participants, reduced error-related PCC deactivation correlated with reduced medication management abilities and mediated the effect of HIV on such abilities. Regarding insular circuitry, we observed interactive HIVxCB effects on rsFC between two anterior insula (aI) subregions and sensorimotor cortices such that, CB use normalized altered rsFC that was observed among non-using PLWH and correlated with decreased somatic complaints and increased inflammation. Finally, regarding large-scale network interactions, PLWH displayed increased salience network (SN)-DMN rsFC that was associated with diminished error-awareness. These results demonstrate that insufficient error-related DMN suppression and heightened SN-DMN rsFC are linked with HIV and have consequences for error-processing and medication management. Additionally, these outcomes suggest a potential normalizing effect of CB on altered insula-sensorimotor neurocircuitries among PLWH and begin to elucidate inflammatory mechanisms through which CB use may impact brain function in the context of HIV

Age-Related Changes in Human Anatomical and Functional Brain Networks

Thesis (Ph.D.) - Indiana University, Psychological and Brain Sciences, 2015i) The first component characterizes age-related changes in specific connections. We find that functional connections within and between intrinsic connectivity networks (ICNs) follow distinct lifespan trajectories. We further characterize these changes in terms of each ICN’s “modularity” and find that most ICNs become less modular (i.e. less segregated) with age. In anatomical networks we find that hub regions are disproportionately affected by age and become less efficiently connected to the rest of the brain. Finally, we find that, with age stronger functional connections are supported by longer (multi-step) anatomical pathways for communication. ii) The second component is concerned with characterizing age-related changes in the boundaries of ICNs. To this end we used a multi-layer variant of modularity maximization to decompose networks into modules at different organizational scales, which we find exhibit scale-specific trends with age. At coarse scales, for example, we find that modules become more segregated whereas modules defined at finer scales become less segregated. We also find that module composition changes with age, and specific areas associated with memory change their module allegiance with age. iii) In the final component we use generative models to uncover wiring rules for the anatomical brain networks. Modeling network growth as a spatial penalty combined with homophily, we find that we can generate synthetic networks with many of the same properties as real-world brain networks. Fitting this model to individuals, we show that the parameter governing the severity of the spatial penalty weakens monotonically with age and that the overall ability to reproduce realistic connectomes for older individuals suffers. These results suggest that, with age, additional constraints may play an important role in shaping the topology of brain structural networks