983 research outputs found

    Time-domain Classification of the Brain Reward System: Analysis of Natural- and Drug-Reward Driven Local Field Potential Signals in Hippocampus and Nucleus Accumbens

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    Addiction is a major public health concern characterized by compulsive reward-seeking behavior. The excitatory glutamatergic signals from the hippocampus (HIP) to the Nucleus accumbens (NAc) mediate learned behavior in addiction. Limited comparative studies have investigated the neural pathways activated by natural and unnatural reward sources. This study has evaluated neural activities in HIP and NAc associated with food (natural) and morphine (drug) reward sources using local field potential (LFP). We developed novel approaches to classify LFP signals into the source of reward and recorded regions by considering the time-domain feature of these signals. Proposed methods included a validation step of the LFP signals using autocorrelation, Lyapunov exponent and Hurst exponent to assess the meaningful stability of these signals (lack of chaos). By utilizing the probability density function (PDF) of LFP signals and applying Kullback-Leibler divergence (KLD), data were classified to the source of the reward. Also, HIP and NAc regions were visually separated and classified using the symmetrized dot pattern technique, which can be applied in real-time to ensure the deep brain region of interest is being targeted accurately during LFP recording. We believe our method provides a computationally light and fast, real-time signal analysis approach with real-world implementation.Comment: 12 pages, 7 figures first two authors contributed equally to this wor

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Trends in recurrence analysis of dynamical systems

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    The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

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    Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

    Pediatric Physiologically Based Pharmacokinetic (PBPK) Modeling to Advance Knowledge of Breastfeeding Infant Exposure to Maternal Medications

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    While there are benefits of breastfeeding to the maternal-infant pair, mothers taking medication may decide not to breastfeed amid unclear risks of exposing the infant to the drug through milk. Uncertainty arises mainly due to the fact that lactating mothers and breastfeeding infants are excluded in the drug development process. In lieu of necessary data for decision making, existing resources include metrics to help estimate risk to the breastfed infant and informational resources that aim to gather all sparsely available information in databases to increase accessibility and empower healthcare providers with knowledge. Current metrics such as the relative infant dose, solely estimate the dose the infant would intake. Before better understanding the potential adverse events an infant might experience (response), a step further to understand exposure is paramount. Yet, the availability of exposure information is difficult to ascertain due to the lack of critical information on the pharmacokinetics (PK; movement of drugs in the body describing dose to exposure) of drug secretion into breast milk, and the resultant levels or exposure of the drug in infant plasma. Physiologically based pharmacokinetic (PBPK) modeling is a promising tool to fill in the gap of scant maternal medication exposure information in breastfeeding infants. PBPK models use a simulation-based approach to model drug kinetics in an organism using knowledge of anatomy and physiology and the physicochemical properties of the drug. Pediatric PBPK models can be developed with minimal a priori data in children because these models rely on a mechanistic understanding of the disposition of the drug typically learned from rich adult data. Thus, despite the lack of available data on drug PK in infants, pediatric PBPK modeling can be used to simulate virtual breastfeeding infant populations to predict exposure given proper estimated doses. The aim of this thesis is to use PBPK modeling to produce a novel risk metric that advances the knowledge of breastfeeding infant exposure to maternal medications. The objectives are to (1) create and apply a workflow incorporating pediatric PBPK modeling to develop the novel metric with infants breastfed from mothers taking lamotrigine, cannabidiol (CBD), and ezetimibe, (2) identify potential maternal factors that may impact concentrations of drugs in milk for incorporation into the workflow established in objective 1 for CBD, and (3) optimize the utility of the novel metric for use in clinical practice. To arrive at the first objective, a literature review was used to develop a model to describe the weight-normalized volume of intake infants typically receive. The model was then used in combination with literature (lamotrigine) or collaborator collected (CBD and ezetimibe) drug concentrations in breast milk to estimate infant daily doses. The doses were then given to virtual breastfeeding infants created through developed and evaluated pediatric PBPK models. For the second objective, linear regression was used to identify influential maternal factors on CBD milk concentrations and breastfeeding exposure predictions. Finally, qualitative interviews were conducted with healthcare providers to ascertain perspectives on the novel metric for use in practice. Through this work, a milk intake model described weight-normalized milk intake with a maximum of 152.6 mg/kg/day at 19.7 days postnatal age. The greatest risk for breastfeeding infant exposure to maternal medications occurred during the 2-4 week postnatal age window. Pediatric PBPK models were developed for lamotrigine, CBD, and ezetimibe. For CBD, literature in vitro data informed the identity and percent contributions of metabolizing enzymes to clearance. These contributions were ascertained as UGT1A7 4%, UGT1A9 16%, UGT2B7 10%, CYP3A4 38%, CYP2C19 21%, and CYP2C9 11%. This information was used to populate the CBD pediatric PBPK model. Results from the linear regression analysis with maternal factors, including administration type, dose-frequency of use, and time after last dose of CBD, revealed that oil or pipe and joint/blunt or edible administrations produced the highest and lowest CBD concentrations in milk, respectively. Overall, the three PBPK models were able to adequately predict exposures of the drug administered in children. A novel risk metric termed the upper area under the curve ratio (UAR) was developed to describe the 95th percentile of breastfed infant AUC divided by the median therapeutic AUC of adults or children for approved indications. Across all ages (0-1 years old), the UAR ranged from 0.18-0.44, 0.00022-0.0044, and 0.0015-0.0026 for lamotrigine, CBD, and ezetimibe, respectively. From the qualitative interviews with 28 healthcare providers, six main themes emerged: (1) Current Practice Approaches, (2) Advantages of Existing Resources, (3) Disadvantages of Existing Resources, (4) Advantages of the UAR, (5) Disadvantages of the UAR, and (6) Strategies to Improve the UAR. Multiple strategies to improve the UAR, such as combining the UAR with another resource and providing guidance to interpret the UAR were attained. The work in this thesis developed the UAR to account for the relative exposure of breastfeeding infants to maternal medications and identify potential outliers who may be most vulnerable. Through healthcare provider interviews, it was evident that the UAR confers benefits over existing metrics and can be optimized for use in practice. With the workflow applied to further drugs, the UAR has the potential to improve our understanding of drug exposures in breastfeeding infants and be used by healthcare providers in their advising
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