Multi-Modal Neuroimaging Analysis and Visualization Tool (MMVT)

Sophisticated visualization tools are essential for the presentation and exploration of human neuroimaging data. While two-dimensional orthogonal views of neuroimaging data are conventionally used to display activity and statistical analysis, three-dimensional (3D) representation is useful for showing the spatial distribution of a functional network, as well as its temporal evolution. For these purposes, there is currently no open-source, 3D neuroimaging tool that can simultaneously visualize desired combinations of MRI, CT, EEG, MEG, fMRI, PET, and intracranial EEG (i.e., ECoG, depth electrodes, and DBS). Here we present the Multi-Modal Visualization Tool (MMVT), which is designed for researchers to interact with their neuroimaging functional and anatomical data through simultaneous visualization of these existing imaging modalities. MMVT contains two separate modules: The first is an add-on to the open-source, 3D-rendering program Blender. It is an interactive graphical interface that enables users to simultaneously visualize multi-modality functional and statistical data on cortical and subcortical surfaces as well as MEEG sensors and intracranial electrodes. This tool also enables highly accurate 3D visualization of neuroanatomy, including the location of invasive electrodes relative to brain structures. The second module includes complete stand-alone pre-processing pipelines, from raw data to statistical maps. Each of the modules and module features can be integrated, separate from the tool, into existing data pipelines. This gives the tool a distinct advantage in both clinical and research domains as each has highly specialized visual and processing needs. MMVT leverages open-source software to build a comprehensive tool for data visualization and exploration.Comment: 29 pages, 10 figure

Feedback of real-time fMRI signals: From concepts and principles to therapeutic interventions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe feedback of real-time functional magnetic resonance imaging (rtfMRI) signals, dubbed “neurofeedback”, has found applications in the treatment of clinical disorders and enhancement of brain performance. However, knowledge of the basic underlying mechanism on which neurofeedback is based is rather limited. This article introduces the concepts, principles and characteristics of feedback control systems and its applications to electroencephalography (EEG) and rtfMRI signals. Insight into the underlying mechanisms of feedback systems may lead to the development of novel feedback protocols and subsystems for rtfMRI and enhance therapeutic solutions for clinical interventions

Neural and behavioral bases of innate behaviors

Recently, ethological studies of animal behavior uncovered its complexity while neuroscientific work began unraveling the neural bases of behavior. Improvements in algorithmic understanding of behavior and neural function contributed to re- cent breakthroughs in robotics and artificial intelligence systems. Yet, animals’ decision-making and motor-control are unequalled by human engineered systems and the continued investigation of the behavioral and neural bases of these abilities is crucial for understanding brain function and inform further technological devel- opments. In my PhD work, I first investigate escape path selection in mice presented with threat, demonstrating how mice combined rapidly acquired spatial knowledge with an innate choice heuristic to inform decision-making. This strategy minimizes the requirement for trial-and-error learning and yields accurate decision-making by combining knowledge acquired at an evolutionarily time-scale with that acquired by the individual. Future work aimed at understanding how these sources of in- formation are combined in the brain to inform decision-making may lead to more efficient artificial learning agents. Next, I studied goal-directed locomotion behav- ior in which mice move rapidly through an environment to reach a goal location. Successful goal-directed locomotion behavior requires substantial navigation and motor control skills and, additionally, sophisticated planning and control of move- ments while moving at high speed. Detailed behavioral quantification and compar- ison to a control-theoretic model demonstrated that mice do possess such planning skills, allowing them to execute rapid and efficient trajectories to a goal. Population- level extracellular recordings of neural activity during goal directed locomotion was also used to begin uncovering the neural bases of planning during locomotion. Altogether, my work combined accurate quantification of animal movements with the- oretical models of optimal behavior to understand behavior at a computation level, aiming to provide crucial information to inform future studies on the neural bases of innate behaviors and aid in the development of novel artificial learning system

Noninvasive Neuromodulation: Modeling and Analysis of Transcranial Brain Stimulation with Applications to Electric and Magnetic Seizure Therapy

Bridging the fields of engineering and psychiatry, this dissertation proposes a novel framework for the rational dosing of electric and magnetic seizure therapy, including electroconvulsive therapy (ECT) and magnetic seizure therapy (MST), for the treatment of psychiatric disorders such as medication resistant major depression and schizophrenia. The objective of this dissertation is to develop computational modeling tools that allow ECT and MST stimulation paradigms to be biophysically optimized ex vivo, prior to testing safety and efficacy in preclinical and clinical trials. Despite therapeutic advances, treatment resistant depression (TRD) remains a largely unmet clinical need. ECT is highly effective for TRD, but its side effects limit its real-world clinical utility. Modifications of treatment technique (e.g., electrode placement, stimulus parameters, novel paradigms such as MST) significantly improve the tolerability of convulsive therapy. However, we know relatively little about the distribution of the electric field (E-field) induced in the brain to inform spatial targeting of ECT and MST. Lacking an understanding of biophysical and physiological mechanisms, refinements in ECT/MST technique rely exclusively on time-consuming and costly clinical trials. Consequently, key questions remain unanswered about how to position the ECT electrodes or MST coil for targeted brain stimulation. Addressing this knowledge gap, this dissertation proposes a new platform that will inform an improved spatial targeting of ECT and MST through state-of-the-art computer simulations of the E-field distribution in human and nonhuman primate (NHP) brain. Part I of this dissertation aims to develop anatomically realistic finite element models of transcranial electric and magnetic stimulation in human and NHPs incorporating tissue heterogeneity and anisotropy derived from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data. The NHP models of ECT and MST are created alongside the human model since NHPs are used in preclinical studies on the mechanisms of seizure therapy. Part II of this dissertation aims to apply the model developed in Part I to electric and magnetic seizure therapy. We compute the strength and spatial distributions of the E-field induced in the brain by various ECT and MST paradigms. The relative E-field strength among various regions of interest (ROIs) is examined to select electrode/coil configurations that produce most focal stimulation of target ROIs that are considered to mediate the therapeutic action of ECT and MST. Since E-field alone is insufficient to account for individual differences in neurophysiological response, we calibrate the E-field maps relative to the neural activation threshold via in vivo measurements of the corticospinal tract response to single pulses (motor threshold, MT). We derive an empirical estimate of the neural activation threshold by coupling simulated E-field strength with individually measured MT. The E-field strength relative to an empirical neural activation threshold and corresponding volume of suprathreshold stimulation (focality) is examined to inform the selection of ECT and MST stimulus pulse amplitude that will result in focal ROI stimulation. We contrast the ECT/MST stimulation strength and focality with conventional fixed and individually titrated pulse amplitude necessary to induce a seizure (seizure threshold, ST) to study pulse amplitude adjustment as a novel means of controlling stimulation strength and focality. This work provides a basis for rational dosing of seizure therapies that could help improve their risk/benefit ratio and guide the development of safer alternatives for patients with severe psychiatric disorders

Variability of head tissues’ conductivities and their impact in electrical brain activity research

The presented thesis endeavoured to establish the impact that the variability in electrical conductivity of human head tissues has on electrical brain imaging research, particularly transcranial direct current stimulation (tDCS) and electroencephalography (EEG). A systematic meta-analysis was firstly conducted to determine the consistency of reported measurements, revealing significant deviations in electrical conductivity measurements predominantly for the scalp, skull, GM, and WM. Found to be of particular importance was the variability of skull conductivity, which consists of multiple layers and bone compositions, each with differing conductivity. Moreover, the conductivity of the skull was suggested to decline with participant age and hypothesised to correspondingly impact tDCS induced fields. As expected, the propositioned decline in the equivalent (homogeneous) skull conductivity as a function of age resulted in reduced tDCS fields. A further EEG analysis also revealed, neglecting the presence of adult sutures and deviation in proportion of spongiform and compact bone distribution throughout the skull, ensued significant errors in EEG forward and inverse solutions. Thus, incorporating geometrically accurate and precise volume conductors of the skull was considered as essential for EEG forward analysis and source localisation and tDCS application. This was an overarching conclusion of the presented thesis. Individualised head models, particularly of the skull, accounting for participant age, the presence of sutures and deviation in bone composition distribution are imperative for electrical brain imaging. Additionally, it was shown that in vivo, individualised measurements of skull conductivity are further required to fully understand the relationship between conductivity and participant demographics, suture closure, bone compositions, skull thickness and additional factors

Brain and Human Body Modeling 2020

​This open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical

Scalable Tools for Information Extraction and Causal Modeling of Neural Data

Systems neuroscience has entered in the past 20 years into an era that one might call "large scale systems neuroscience". From tuning curves and single neuron recordings there has been a conceptual shift towards a more holistic understanding of how the neural circuits work and as a result how their representations produce neural tunings. With the introduction of a plethora of datasets in various scales, modalities, animals, and systems; we as a community have witnessed invaluable insights that can be gained from the collective view of a neural circuit which was not possible with small scale experimentation. The concurrency of the advances in neural recordings such as the production of wide field imaging technologies and neuropixels with the developments in statistical machine learning and specifically deep learning has brought system neuroscience one step closer to data science. With this abundance of data, the need for developing computational models has become crucial. We need to make sense of the data, and thus we need to build models that are constrained up to the acceptable amount of biological detail and probe those models in search of neural mechanisms. This thesis consists of sections covering a wide range of ideas from computer vision, statistics, machine learning, and dynamical systems. But all of these ideas share a common purpose, which is to help automate neuroscientific experimentation process in different levels. In chapters 1, 2, and 3, I develop tools that automate the process of extracting useful information from raw neuroscience data in the model organism C. elegans. The goal of this is to avoid manual labor and pave the way for high throughput data collection aiming at better quantification of variability across the population of worms. Due to its high level of structural and functional stereotypy, and its relative simplicity, the nematode C. elegans has been an attractive model organism for systems and developmental research. With 383 neurons in males and 302 neurons in hermaphrodites, the positions and function of neurons is remarkably conserved across individuals. Furthermore, C. elegans remains the only organism for which a complete cellular, lineage, and anatomical map of the entire nervous system has been described for both sexes. Here, I describe the analysis pipeline that we developed for the recently proposed NeuroPAL technique in C. elegans. Our proposed pipeline consists of atlas building (chapter 1), registration, segmentation, neural tracking (chapter 2), and signal extraction (chapter 3). I emphasize that categorizing the analysis techniques as a pipeline consisting of the above steps is general and can be applied to virtually every single animal model and emerging imaging modality. I use the language of probabilistic generative modeling and graphical models to communicate the ideas in a rigorous form, therefore some familiarity with those concepts could help the reader navigate through the chapters of this thesis more easily. In chapters 4 and 5 I build models that aim to automate hypothesis testing and causal interrogation of neural circuits. The notion of functional connectivity (FC) has been instrumental in our understanding of how information propagates in a neural circuit. However, an important limitation is that current techniques do not dissociate between causal connections and purely functional connections with no mechanistic correspondence. I start chapter 4 by introducing causal inference as a unifying language for the following chapters. In chapter 4 I define the notion of interventional connectivity (IC) as a way to summarize the effect of stimulation in a neural circuit providing a more mechanistic description of the information flow. I then investigate which functional connectivity metrics are best predictive of IC in simulations and real data. Following this framework, I discuss how stimulations and interventions can be used to improve fitting and generalization properties of time series models. Building on the literature of model identification and active causal discovery I develop a switching time series model and a method for finding stimulation patterns that help the model to generalize to the vicinity of the observed neural trajectories. Finally in chapter 5 I develop a new FC metric that separates the transferred information from one variable to the other into unique and synergistic sources. In all projects, I have abstracted out concepts that are specific to the datasets at hand and developed the methods in the most general form. This makes the presented methods applicable to a broad range of datasets, potentially leading to new findings. In addition, all projects are accompanied with extensible and documented code packages, allowing theorists to repurpose the modules for novel applications and experimentalists to run analysis on their datasets efficiently and scalably. In summary my main contribution in this thesis are the following: 1) Building the first atlases of hermaphrodite and male C. elegans and developing a generic statistical framework for constructing atlases for a broad range of datasets. 2) Developing a semi-automated analysis pipeline for neural registration, segmentation, and tracking in C. elegans. 3) Extending the framework of non-negative matrix factorization to datasets with deformable motion and developing algorithms for joint tracking and signal demixing from videos of semi-immobilized C. elegans. 4) Defining the notion of interventional connectivity (IC) as a way to summarize the effect of stimulation in a neural circuit and investigating which functional connectivity metrics are best predictive of IC in simulations and real data. 5) Developing a switching time series model and a method for finding stimulation patterns that help the model to generalize to the vicinity of the observed neural trajectories. 6) Developing a new functional connectivity metric that separates the transferred information from one variable to the other into unique and synergistic sources. 7) Implementing extensible, well documented, open source code packages for each of the above contributions