Lucas Parra - US grants

DESCRIPTION (provided by applicant): Intellectual Merit: There is compelling evidence that the distinct stages of sleep play an essential role in the long-term consolidation of memories (Marshall &Born 2007). Specifically, slow-wave sleep (SWS), which is hallmarked by slow oscillatory activity (<1 Hz) in the human electro-encephalogram (EEG), has been implicated in memory consolidation. We demonstrated that weak electric currents (<1mA, <1Hz and DC) applied to the scalp during SWS modulate these endogenous EEG rhythms and can improve human memory performance (Marshall 2006a). Moreover, application of the same weak currents during learning modulates ongoing EEG rhythms that are typical for the awake state in humans and boosts immediate performance in some learning tasks (Kirov 2009). Yet, despite these remarkable phenomenological findings, the question of how weak currents can modulate brain oscillations and induce plastic changes in brain function remains fundamentally unaddressed. Here we propose to quantitatively address this question through the development of computational models that are tightly constrained by specialized brain-slice experiments and validated through targeted human subject experiments. A central question is: how can weak electric currents, that appear insufficient to modulate excitability or plasticity in quiescent neurons, exert such a powerful effect on oscillations and learning? Our central hypothesis is that weak currents couple into ongoing slow oscillatory activity that then boost their modulatory effect on synaptic plasticity. Preliminary data from our group and others already provides strong evidence for modulation of endogenous rhythmic network activity by applied currents - at intensities considered too weak to affect single neuron function. Concurrently, we and other groups have investigated links between slow wave activity and memory consolidation, including by application of weak currents in human. But a specific connection between the effects of applied weak currents on slow-wave rhythms and plasticity has so far not been explored. Guided by computational models, the crucial empirical link between the two will be sought by probing lasting changes resulting from weak-current stimulation of an in vitro cortical preparation that exhibits SWA. Targeted human experiments will directly test if applied currents also enhance the consolidation of other SWS-mediated learning as the hypothesis would suggest, or rather, if the effect is limited to hippocampus-related learning, thus providing significant constraints to the computational models. Broader Impacts: Weak applied currents are being explored in a number of empirical studies for their potential benefits to treat depression and neuropathic pain, to assist motor learning after stroke, or more generally, to enhance cognitive performance and to improve learning. The promise of this technique is that weak currents can be applied non-invasively with a potentially broad range of applications and minimal side effects. The enigma in this potentially transformative clinical tool, however, is that the electric field strengths generated by these currents in most studies are two orders of magnitude below what is required to activate an otherwise silent neuron. Currently, research in this area is almost entirely phenomenological and the few mechanistic explanations for the promising phenomenological observations are superficial (e.g. describing all brain function as a "sliding scale of excitability") and do not address plasticity - as such, there is no rational basis for improving and targeting stimulation protocols. This work is the first attempt at establishing the mechanistic link between applied currents on endogenous rhythms and the associated SWS-related learning enhancements. Evidently, such an analysis will address basic science questions about the link between endogenous SWS and learning, add to the set of experimental tools which can be used to study cognition, and, shed light on the functional and causal role of the ubiquitous endogenous rhythms generated by the brain. Consistent with present call for US/German Collaborative Research in Computational Neuroscience this project will combined the expertise of international researchers in the areas of: (1) effects of noninvasive electrical stimulation on nervous tissue (Bikson, US), EEG signal analysis and computational network models (Parra, US), human sleep and learning with applied currents (Marshall, Germany), and dynamical systems and machine learning (Claussen/Martinetz, Germany;Parra, US).

DESCRIPTION (provided by applicant): Humans have a natural and intuitive tendency to direct attention and to act upon objects of our environment that we value most. However, we often need to overcome this inclination to act in an appropriate manner. A compromised ability to exert such control lies at the root of major psychopathologies such as depression, addiction and obsessive-compulsive disorder. The overarching aim of our project is to establish well-defined human neurophysiological indices that open a direct window on the processes of attention, intention, and value, their dynamics and their dissociability. This will not only provid insight into inherently human aspects of decision making, but will also lead to clinical studies that can furnish a mechanistic account of disorders of value-based decision making. In preliminary work we examined the neural signatures of relative value encoding in a simple task where two eye movement (saccade) target alternatives are presented in the form of colored discs appearing on the left and right of a display, before a final action instruction is provided. e identified a robust, transient, electrophysiological index of the relative valuation of alternative action targets in space, observed in the visual evoked potential to the targets. Here, we propose a series of eight simple experiments to test the generality of this signal across motor and sensory parameters and more fully elucidate the nature of this process specifically in relation to the allocation of attention and the formation of an intentional motor plan. Experiment 1 will assess whether the signal encodes the magnitude of the relative value differential in a valence-independent manner by substituting losses for gains in the original task design. Experiment 2 and 3 will test the visual feature and motor output specificities of our signal by using different shapes instead of colors (exp 2) and button-presses instead of saccades (exp 3). In experiment 4 we will remove the speed pressure by lifting the movement execution deadline to determine the influence of this aspect of motor intention. Experiments 5 and 6 will determine whether the relative value representation is echoed on the presentation of target alternatives even when the identity and value of the target is known in advance (exp 5: color pre-cue, color-value), or alternatively, when the value is fixed to the target location (exp 6: color pre-cue, spatial-value) In experiments 7 and 8, we will address the scenario where the target location is known in advance and employ the same color (exp 7: spatial pre-cue, color-value) and spatial value associations (exp 8: spatial pre- cue, spatial-value). The completion of these experiments and the consequent clarification of value-associated target selection mechanisms will provide a well-grounded platform for conducting carefully designed studies of these processes in depression and addiction.

The goal of this project is to investigate the signature of mental states, using the induction of transitions among states as an experimental strategy. The work is driven by a novel network theory -- Network of Networks (NoN) -- that emphasizes the importance of weak links. The project will identify a new network paradigm with the concomitant identification of network markers and computations that confer specificity and robustness to information transmission and gating between different processing modules. The prospective interventions are designed to provide strong empirical constraints on theories of brain network structure beyond the specific NoN hypothesis tested here, including alternate network models, e.g "scale-free" and "rich-club," in order to obtain falsifiable predictions for the prospective interventions. The results can also be readily applied to other systems ranging from metabolic, protein and genetic networks to social networks and the Internet.

The present study will test specific predictions of network theory with regards to the correlation structure of in-vivo functional magnetic resonance (fMRI) and local field potential (LFP) recordings. The investigators will determine network structure and location of key nodes in the brain network topology by analyzing the structural connectivity based on Connectome data from rodents combined with functional connectivity. The proposed NoN framework will reveal the location of such key nodes -- "superspreaders" and "superinhibitors" -- and make predictions on cascading neural activity, robustness and vulnerability of the brain network.

? DESCRIPTION (provided by applicant): Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that applies weak electric currents to the head. tDCS is proposed to modulate cognitive function with few known side effects and is under investigation for the treatment of diverse neurological or psychiatric conditions such as pain, depression, and stroke. The conventional assumption for the mechanism of action of tDCS is that a positively charged electrode increases cortical 'excitability' and this 'enhances' function attributed to the targeted cortical area. However, this simplistic excitability assumption does not explain the diversity across studies and specificity within studies of reported cognitive effects, and has not been reliable at predicting outcomes of clinical trials. To guide and accelerate the development of new treatment protocols, it is important to clarify the cellular mechanisms of direct current stimulatin (DCS). We propose that DCS acts via a modulation of endogenous synaptic plasticity mechanisms. Support for this comes from pharmacological experiments in humans as well as direct evidence that DCS can boost synaptic plasticity in brain slices. The goal of this proposal is to determine the cellular mechanisms by which DCS modulates long-term synaptic plasticity. We have recently demonstrated robust effects of DCS on long-term potentiation (LTP) and long-term depression (LTD) using standard plasticity induction protocols such as tetanus and theta burst stimulation. We will probe well-established cellular mechanisms of these induction protocols in hippocampal slices, which provide unique control of the effects of stimulation on different cellular compartments. In Aim 1 we explore the specific hypothesis that DCS modulates LTP/LTD by polarizing dendrites directly affecting calcium dynamics through voltage dependent calcium channels. In Aim 2 we test the specific hypothesis that DCS modulates LTP by polarizing cell somata, thus modulating post-synaptic firing rate. A series of predictions that result from these specific hypotheses will be tested using two-photon calcium imaging, stimulation and recordings from multiple pathways, patch-clamp recordings, and pharmacological interventions to determine involvement of calcium and sodium channels as well as neuro-modulators such as brain-derived neurotropic factor (BDNF). Finally, all experimental results will be synthesized in biophysically realistic computational models. Our basic proposal provides a mechanistic explanation for observed functional specificity, because only networks undergoing plasticity are boosted by DCS. Importantly, if confirmed, our specific hypotheses link the mechanisms of DCS with well-established mechanisms of LTD/LTP, which are in turn linked to learning and disease. This has important clinical implications. For instance, it suggests that tDCS will be most effective as an adjunct to behavioral interventions that foster plasticity and it provides answers for clinically relevant questions such as how long the effects of tDCS persist. The results of this project will provide a precise and quantitative framework to understand the cellular mechanistic of DCS, which is required in order to advance the science and treatment of tDCS.

When a teacher is presenting new material during class or online, are students actually paying attention? The ambitious goal of this project is to objectively measure students' attentional engagement during learning using electroencephalography (EEG). Specifically, it measures students' brain activity as they watch online educational videos, and then compares their neural activity to how well they perform on traditional measures of learning such as tests and quizzes. If successful, this project will develop a tool that can assess student attentional engagement at the neural level and predict learning performance. Providing a concrete and practical educational application for brain imaging is important because it can be used to inform the effectiveness of teaching approaches and instructional materials, or intervene in real-time when individual students fail to engage with the material. Such a tool may be particularly important in the context of online education where teachers and students often do not immediately interact with one another. The educational video material will focus on natural sciences and math with the goal of improving STEM education. The project is funded by the EHR Core Research (ECR) program which funds basic research that seeks to understand, build theory to explain, and suggest interventions (and innovations) to address persistent challenges in STEM interest, education, learning, and participation. It is co-funded by the Perception, Action, and Cognition program in the Social, Behavioral, and Economics Sciences Directorate.

Effective learning requires attention to the instructional material. By measuring attention from brain signals, both the efficacy of instruction and the performance of individual students may be assessed. Knowing whether and when students fail to engage attention is crucial for designing better instructional materials or to assist students on an individual basis. The basic hypothesis of this project is that effective instructional materials will generate similar EEG responses across attentive students. The investigators test this hypothesis for STEM educational videos to determine if learning can indeed be predicted. Participants include ~180 undergraduates enrolled in City College of New York's Division of Science and School of Engineering. Educationally, participants also include 30 high school seniors from four diverse New York City high schools. Similarity among students' responses to educational video will be measured as inter-subject correlation (ISC) of EEG activity. The project has three specific aims: (1) Validate ISC using instructional videos; (2) Test the link between ISC and traditional learning outcome measures; and (3) Develop a mobile application to help transfer ISC measures from the laboratory to the classroom setting.

SUMMARY Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis to date. Women with a strong family history or related genetic mutations have an elevated risk of breast cancer and are recommended to participate in yearly MRI screenings. However, the rate of detection in this high-risk cohort is small, prompting a desire to reduce unnecessary MRI exams. The basic hypothesis of this project is that within the screening cohort the individual risk of a future cancer can be estimated based on the appearance of breast MRI and mammograms today. In preliminary work we have already identified low-risk women that could have omitted a screening session without missing a new cancer. The discovery of this lower-risk subgroup was made possible by modern deep-learning tools developed in preliminary work. Memorial Sloan Kettering Cancer Center (MSK) has accrued a database of approximately 70,000 breast MRI exams over 18 years along with the patients? clinical outcomes. This unprecedented resource enables the training of modern machine learning ?from the ground-up? to extract and classify volumetric MRI features. The specific aims of this project are as follows. Aim 1 (Data curation): Systematic analysis of the large dataset accrued at MSK requires careful curation including image content, image quality, pathology results, clinical follow-up, as well as demographic and genomic information. The outcome of this Aim is a curated dataset that can broadly benefit future technical efforts in breast diagnosis. Aim 2 (Deep learning): To make risk stratification quantitative we propose to analyze the MRI scans using modern deep networks that have been trained to identify the location and extent of a cancer. We will then transfer the MRI features of these trained networks as well as networks trained on mammograms to the task of diagnosis and risk assessment. The intended outcome of this Aim are predictive models with human-level performance at diagnosis and segmentation. Aim 3 (Risk adjusted screening): To reduce the burden of screening while maintaining sensitivity we will estimate the risk of finding a malignant tumor in the future, based on the present MRI exam and most recent mammogram as well as patient information. The machine-estimated risk will be used in a retrospective analysis to determine the primary outcome, namely, the number of exams that could have been omitted by scheduling a longer screening interval without compromising sensitivity. This will be repeated on newly accrued data at MSK, Duke and Johns Hopkins University (JHU) as secondary sites. Once validated, the risk-prediction model will be publicly released to encourage data sharing and clinical adoption. The preliminary work performed over the last two years has brought together a unique interdisciplinary team including clinical investigators on breast MRI at MSK, and machine-learning and medical imaging experts at CCNY, Duke and JHU. The platform technology that will be developed here is applicable beyond breast cancer, and the transfer learning approach applicable in particular to cancers with more limited datasets.

Education is moving online. During the global pandemic, this ongoing process accelerated and educators increasingly leveraged existing online video content to supplement synchronous remote instruction. One problem with this approach is that passive viewing of video content is not a particularly effective form of instruction. Many students struggle to sustain their attention to online video, and this is reflected in poor performance in subsequent tests of learning. This project will explore a variety of interventions that aim to engage the students with such video material. These interventions include such characteristics as interleaving questions and feedback, prompting constructive thinking and facilitating discussion with other students. The premise is that active viewing enhances attention and leads to better learning outcomes. This laboratory research aims to refine the concept of “student engagement”, which at present does not distinguish between active participation and attention to the learning material, two important but potentially differing factors that contribute to learning. This fundamental research on learning is made possible by measuring attentional engagement directly using brain and eye activity. From a practical point of view, the goal of this project is to identify simple interventions teachers can use to better leverage the vast trove of educational videos available online. <br/><br/>The overall objective of this project is to improve the efficacy of video in remote instruction. The basic hypothesis is that attentional engagement with educational video mediates learning and that attentional engagement can be enhanced with interventions that promote active viewing. This hypothesis motivates the following aims. Aim 1: Test interventions that convert passive viewing into active viewing to improve learning: Specifically, identify interventions that improve attention and learning with existing short STEM education videos. Interventions will be tested prospectively on an online platform while recruiting participants online and among STEM college students. Aim 2: Explain the effects of attentional engagement with video on learning: Specifically, test the basic hypothesis by determining whether the effect of successful active viewing interventions prospectively affect attentional engagement and performance. Attention will be measured with eye tracking in remote experiments and with EEG in the lab, while recruiting among STEM college students. The project will also test the effect of cognitive traits and the learning environment. Aim 3: Include students from groups underrepresented in STEM fields in the research. Specifically, students will assist in research with eye tracking and EEG, as well as the selection of video material and design of test questions. In doing so, these student researchers will provide their unique perspective on the challenges of remote learning.<br/><br/>This project is funded by the EHR Core Research (ECR) program, which supports fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.