The Alviña lab is interested in investigating neural mechanisms altered by environmental factors such as stress, dietary habits, and exercise. We are also interested in uncovering how these mechanisms can sometimes lead to unhealthy cognitive aging and neurodegenerative disorders, and social behavior disruption such as that observed in autism spectrum disorder.
The ERS-AI PhD student will be working on research projects that are consistent with our long-term goal of implementation of artificial intelligence for autonomous phenotyping and communication of patient’s condition in a fair, and reproducible manner based on multimodal data. The PhD student will be working on multiple projects to provide them with opportunities to gain experience with different types of data and using data from different domains such as nephrology and critical care utilizing different type of techniques.
Higher cognitive functions that decline in old age and the early stages of AD, such as memory and executive functions, are supported by neural networks distributed across the medial temporal lobe (MTL) and prefrontal cortex (PFC). Critically, these structures are among the earliest to accumulate pathology in AD, of which aging is the single greatest risk factor. While the precise mechanisms that render the aged brain vulnerable to neurodegeneration remain to be determined, it is known that aging is associated with a host of regionally specific neurobiological alterations within the PFC and MTL that do not correlate. This fact presents a major challenge for the development of effective therapeutics because higher cognition is supported by networks distributed across these vulnerable areas. Thus, targeted interventions that restore function in one brain region may neglect or exacerbate dysfunction in another, hindering the restoration of normal cognition. As such, interventions that target the optimization of “cognitive networks” rather than discrete brain regions may be more effective for improving behavioral outcomes in older adults. In order to do this, we need new technologies that can link cellular changes at the microscopic level to global changes in macroscopic brain networks that cooperate to support higher cognitive function. A current focus of my research program that implements artificial intelligence is developing methods that can link cellular changes to global brain connectivity through machine learning that can co-register different imaging platforms and classify cellular activity.
Neuroinflammation that results from chronic inflammatory states is recognized as a key driver of Alzheimer’s disease (AD). The exposome, which includes poor diet, is regarded as an important source of chronic inflammation and late-onset AD risk. In fact, obesity-induced type 2 diabetes (T2D), is the highest risk factor for late-onset AD behind aging. However, the mechanistic processes that link chronic metabolic stress to neuroinflammation and AD progression are not fully known. Addressing this relationship using a targeted hypothesis-based approach is difficult given the multifactorial nature of potential interactions (multiple disease factors, time, and sex). This is further confounded by changing and/or incomplete experimental design factors across animal studies. Therefore, in our lab we are trying to tackle these challenges by studying the relationship between METS/T2D progression and AD in a more global genome-wide manner using standard bioinformatics tools to comb through RNAseq pathways and novel gene targets and developing machine learning approaches, including analyses of sparse matrices, to combine RNAseq data with AD and METS-related neurodegenerative and metabolic changes, in both sexes, and over time that allows for more complex comparisons to identify associations across a group of variables and potentially novel targets. We also use these tools to create snapshot representations of complicated mechanisms such as RNA editing that have been observed in AD but been unable to connect to disease pathogenesis in a tangible way.
More than half of the~275,000global, annual, traumatic spinal cord injuries (SCI) occur at the cervical level, leading to paralysis and respiratory compromise or failure. Approximately 20-30% of cervical SCI(cSCI) patients will require ventilator support for which there are very few therapeutic options for recovery. Indeed, the leading cause of morbidity and mortality after cSCI is respiratory compromise. Even in cases where mechanical ventilation is not required, many people with SCI are unable to cough to clear their airways and thus die of pneumonia. Acute epidural electrical stimulation has emerged as a strategy to restore vital motor, sensory, and autonomic functions in both experimental and clinical settings after SCI. For example, after spinal injury, epidural stimulation improves cardiovascular, bladder and trunk stability via neuromodulation of spinal neural networks. And more recently, we have shown modest success in eliciting respiratory neuroplasticity in the spinal neural network controlling breathing after short-term epidural stimulation in rats. Though limited underlying mechanisms have been proposed, to date little is known how epidural stimulation elicits this motor function at the neuronal level. Even less is known about the capacity for epidural stimulation to promote long-lasting recovery and device-independence nor by which stimulation paradigms this could occur. Thus, it is imperative to functionally map the stimulation parameter space in order to characterize and optimize recovery.
The Giusti-Rodriguez Lab works at the intersection of neuroscience, human genetics, and functional genomics, and aims to maximize the tools and techniques of these fields to advance our understanding of the genetics of neuropsychiatric disorders. AI in genomics is growing rapidly, and deep learning methods have been applied to the analysis of diverse datatypes, including DNA and RNA-sequencing, methylation, DNA accessibility and chromatin, and 3Dgenome organization. The Giusti-Rodríguez lab will generate diverse data types using mouse, postmortem human brain tissue, iPSCs, etc., and has access to many external datasets through existing collaborations and or publicly available datasets. The Giusti-Rodríguez lab will apply machine learning and artificial intelligence approaches to multiomics datatypes relevant to understanding specific susceptibilities to psychiatric disorders and to parse out genetic underpinnings in individuals from diverse populations and complex admixture.
Landmark scientific discoveries support the neural population doctrine, where the neuronal population, not the single neuron, are the essential unit of computation in many brain regions. New computing technologies have enabled neuroscience research at the level of the neural population. The long-term goal of our research is to apply artificial intelligence to the analysis of dopamine neural populations to decode neural dynamics. We recently employed live-cell calcium imaging in the midbrain slices of DAT-cre/loxP-GCaMP6f (DAT-GCaMP6f) mice of either sex and computational analyses to show that functional network connectivity greatly differs between substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) regions. Using complex network analysis, we found a higher incidence of hyperconnected (i.e. hub-like) neurons in the VTA than the SNc. The lower number of hyperconnected neurons in the SNc is consistent with the interpretation of a lower dopamine neuronal network resilience to the SNc’s neuronal loss-implicated in neurological disorders. Our ongoing studies expand this work to in vivo studies in freely moving DAT-GCaMP6f mice of either sex via live cell calcim imaging through microendoscopic lenses. This approach enables imaging of previously inaccessible dopamine neuronal populations deep within the midbrain of freely moving animals exposed to saline or methamphetamine.
Dr. Lamb is an assistant professor of Psychiatry at the University of Florida and a Health Research Scientist at the Brain Rehabilitation Research Center at the Malcom Randall VAMC in Gainesville, FL. He is interested in the complex interaction of autonomics, emotional function and cognition. His undergraduate training was at the University of Maryland in Computer Engineering and Mathematics. He then earned a Master of Science in Computer Science from the University of Chicago and his PhD in Neuroscience from Emory University, where he focused on biophysical computational modeling of autonomic neuronal networks. He conducts clinical-translational research and education in human neuroimaging of psychiatric and related disorders.
Breastfeeding is associated with positive maternal-child health outcomes that includes reducing transmission of non-HIV infection such as COVID-19 from mother to baby. In the United States, rates of breastfeeding differ significantly depending on race and income status of mother. Mothers with lower rates of breastfeeding tend to be young, low-income, African American, unmarried, less educated, participants in the Supplemental Nutrition Program for Women, Infants, and Children (WIC),overweight or obese before pregnancy, and more likely to report their pregnancy was unintended. Given the complexity in breastfeeding disparities, there is an urgent need to develop breastfeeding interventions that include vulnerable and hard-to-reach populations. Electronic health records(EHRs) represent a unique data source that contains longitudinal clinical data that is linked to non-clinical data sources such as residential location, race, socio-economic status and other social determinants of health (SDoH).The goal of this project is to leverage mom-baby linked EHR to estimate geospatial patterns in breastfeeding and characterize the SDoH that impact breastfeeding outcomes invulnerable and hard-to-reach populations.
Our proposed project leverages the utility of artificial intelligence (AI) to personalize management of patients with cardiogenic shock. Cardiogenic shock is a very serious clinical situation that occurs when a patient’s heart cannot pump sufficient blood and oxygen, which can lead to failure of other organs such as the lung, brain, kidney, and liver. This is a medical emergency requiring an immediate treatment. The most effective therapy for this critically ill cohort is heart transplantation (HTx). However, HTx is a highly complex process and a major outcome determinant that requires interactions among multiple advanced specialties over a considerable amount of time (often weeks to months) to identify an appropriate donor and a mechanical circulatory support (MCS) device to “bridge” the patient over this time period to achieve medical stability until a suitable donor heart is identified. Selection of the appropriate “bridging strategy” to HTx is one of the main challenges. The current clinical practice relies on subjective patient and provider experiences with few broad MCS principles. This state of evidence scarcity results in bias, care disparity, heuristics, decision fatigue, and is recognized as an outcome limitation. Our central hypothesisis that using AI to develop a data-driven precision medicine approach for this complex and heterogeneous patient cohort can enhance the clinical practice and result in better outcomes. Additionally, building a graphical platform that shows an interactive presentation of each MCS platform can promote shared decision-making between patients and clinicians. To the best of knowledge, our proposal is the first effort to utilize advanced computational models and a data-driven approach to guide heart failure and replacement therapies.
Dr. Merritt’s project uses AI approaches, primarily neural networks, for automated quantitation and denoising of nuclear magnetic resonance (NMR) data. AI has well known abilities for performing image recognition, and by its very nature, a neural network can evaluate a target image almost instantaneously once it is trained. The speed and robustness of neural network approaches suggest that its application to the spectra denoising/fitting and quantitation problem in NMR could be very profitable. Initial results using a deep learning neural network produced an increase in signal-to-noise ratio (SNR) of 200to 1for13C NMR spectra(1).Using traditional Fourier transformation methods, the SNR is proportional to (square root of # of scans) the which means that it takes 4 times the number of scans to give twice the SNR. A gain in SNR of 200 is equivalent to running the same sample 40000 times longer. Given that most13C spectra acquired in my lab take at least 6 hours to acquire, the time savings possible with this approach are truly transformational.
The Padilla-Coreano Lab studies how the brain facilitates social behaviors using tools at the intersection of neuroscience and Artificial Intelligence. Specifically, the lab studies the neural mechanisms of social competence, that is how we adjust our social behavior based on information, using mouse models. Two key elements of this research goal are: being able to measure social behaviors and understanding the relationship between behavior and brain activity. The lab uses Artificial Intelligence to tackle both key elements. The PI is a co-developer of a recent Deep Learning tool (AlphaTracker) that does pose estimation for multiple animal tracking (Padilla-Coreano et al., 2020 preprint). Furthermore, this lab has active collaborations with machine learning scientists at UF to create new tools to analyze behavior incorporating temporal information and structure for unbiased automatic behavior classification. Furthermore, the lab is focused on studying neural function at the network level. By recording neural activity of multiple brain regions simultaneously we can identify what circuits and sequences of circuits lead to important social behaviors. Given the complexity of the data (both neural and behavioral),Artificial Intelligence helps identify the causal relationship between neural activity and behavior. The PI has applied similar approaches to predict behaviors and conditions from neural activity and the lab will expand this approach to consider neural activity from a whole network.
Dr. Sarder’s lab develops novel computational methods to study and understand tissue micro-anatomy using multi-modal whole-slide microscopy images as well as associated molecular omics data. Our method facilitates decision making in a clinical work-flow (both for diagnosis and predicting progression of diseases), and also allows studying fundamental systems biology of disease dynamics. Currently, our major focus involves studying chronic kidney diseases as well as ‘reference’ organ systems across scale.
The ERS-AI PhD student will be recruited into projects exploring the application of multi-modal foundation models fora variety of clinical applications and patient health modeling. Briefly, foundation models comprise a recent class of large-scale machine learning frameworks based on the Transformer model architecture that are designed to formulate scalable data-driven representations from voluminous data, merging AI principles of supervised, unsupervised, and self-supervised learning techniques; such data representations can be applied to several downstream AI tasks. Currently popularized by innovations in natural language processing (NLP),the ERS-AI PhD student will research the translation of these discoveries into the healthcare domain by developing foundation models of patient health that integrate granular and temporal health data from multiple modalities (e.g. continuous and discrete electronic health record measurements, clinical notes, radiography, omics data) for unified health representations that can be applied to downstream clinical prediction tasks (e.g. sepsis, acute kidney injury, mortality).Methods to measure and improve explainability, fairness, and causality of foundation models will be a large focus of the research projects. The first project to which the student will be recruited will involve the development of a Transformer foundation model for dynamic monitoring of acute kidney injury(AKI).
In the last two decades, the introduction of targeted anticancer therapies has revolutionized the treatment of hematological malignancies such as multiple myeloma, chronic myeloid leukemia, and solid malignancies such as breast and renal carcinoma. Contemporary cancer therapy has led to a 23% reduction in cancer-related mortality rate and a rapid increase in cancer survivorship in the last 15 years. However, some devastating side effects of these treatments have also resulted in increased morbidity and mortality. For example, cardiotoxicity is one of the well-documented adverse events of cancer treatments resulting either from accelerated development of cardiovascular diseases in cancer patients or from the direct effects of the treatment on the structure and function of the heart. The goal of this project is to develop predictive models for the identification of cancer patients with a high risk of cardiotoxicity to prevent or minimize the risk of cardiotoxicity in cancer treatments.
To develop machine learning methods for the identification of Alzheimer’s disease (AD) and its related dementias (ADRD) sub-phenotypes. Using electronic health records (EHRs) from patients diagnosed with AD/ADRD, we will retrospectively review their structured EHRs, clinical notes, and neuroimages and develop machine learning methods for connecting these data sources and computationally deriving AD/ADRD sub-phenotypes based on hierarchical clustering. Interfaces with Data Science/AI: Students are required to develop machine learning methods to connect different data modalities and develop AI methods to derive disease subtypes from large-scale health data.