Vinod Menon - US grants

DESCRIPTION:(provided by applicant) The basal ganglia are thought to play a critical role in adaptive behavior and cognition in general, and movement preparation, initiation, control and sequencing in particular. However, the relative contributions and roles of the various sub-components of the basal ganglia are not known. The overarching goal of this proposal is to characterize the differential contributions of dorsal basal ganglia (dBG) nuclei during the sequential organization of behavior. The proposed study aims to extend and amplify our previous findings of reliable and differential fMRI activation of the anterior caudate, anterior and posterior putamen and globus pallidus during movement sequencing. We plan to investigate in greater detail dBG involvement in various specific components of movement sequencing, and to examine the relationship between dBG activation, overall behavior, and specific behavioral performance measures such as reaction time and accuracy. Although the primary focus of this study is the dBG, the relative signal change, spatial extent and time course of activation in neocortical motor areas and the cerebellum will be also be examined. In addition, functional relations between the posterior putamen and globus pallidus, thalamus, cerebellum and cortical motor areas receiving dBG output will also be examined. Study 1 will investigate dBG function during movement sequencing in relation to specific and measurable changes in behavior. We will examine the effect of rate of movement on dBG activation during sequencing of unpredictable and predictable movements. Study 2 will investigate dBG function in relation to specific components involved in motor control during movement sequencing using event-related fMRI. Together these studies will more convincingly relate dBG to behavior during movement sequencing. More broadly, contrasting the roles of the dBG, motor cortex, SMA, pre-SMA and the cerebellum will yield significant information about distributed brain processes involved in the sequential organization of behavior. Results from the proposed studies will provide additional insights regarding the role of the dBG in adaptive behavior. Furthermore, the proposed research will aid in understanding and treating basal ganglia disorders, such as schizophrenia, ADHD, Huntington's disease, Parkinson's disease, and Tourette's syndrome.

DESCRIPTION (provided by applicant): The candidate for the proposed quantitative career development award has multidisciplinary training and research experience in physics and the computer sciences as well as in neurophysiology and functional brain imaging. The overarching goal of the current proposal is to enhance the candidate's expertise in developmental cognitive neuroscience. Through a multidisciplinary program combining education, mentoring, and the completion of an innovative research study, the candidate seeks to investigate brain function in both typically developing children and children with specific neurodevelopmental disorders. As a result, the proposed program will not only improve the candidate's research skills and expertise, but will also contribute to the discovery of essential knowledge about the normal cognitive development and its disruption. The institutional resources, environment, and opportunities for research and collaboration in functional neuroimaging of children and adults at Stanford University are exemplary. At the end of the award, the candidate expects to: (1) possess the skills needed to be an independent investigator in developmental cognitive neuroscience; (2) to have received funding as an independent investigator; and (3) to become a leader in the scientific study of basic and clinical developmental cognitive and systems neuroscience. As part of this award the candidate will complete a mentored research project titled "Longitudinal fMRI study of cognition in children" under the mentorship of Dr. Allan Reiss at Stanford University. In this proposal, the candidate plans to use fMRI to investigate the typical and atypical patterns of development of cognitive functions in children during a critical stage in the development of higher cognitive function. Twentyfive typically developing children and twentyfive children with Fragile X, a neurodevelopmental disorder, who are 68 yrs old (mean age 7 yrs) at the start of the study will be imaged twice, two years apart. Brain images will be acquired while children perform working memory and arithmetic reasoning tasks. Behavioral performance measures will be acquired simultaneously to determine how well and how efficiently individual children perform these tasks. Standardized neuropsychological. assessment will be performed on each individual to determine the overall level of cognitive ability in each child. The relation between changes in the level and extent of brain activation, task performance and overall IQ will be analyzed to determine domain specific and domain nonspecific features underlying the neurobiology of cognitive development. The proposed fMRI research project will yield important new and more precise information about the neural bases of the development of higher cognitive processes in children as well as its disruption in atypical development.

Dr. Vinod Menon, with funding from the National Science Foundation, and with his collaborator at McGill University, Dr. Daniel Levitin, is studying how the temporal structure in music is processed in the human brain. His work extends to investigating the neural underpinnings of pattern perception in general. His project is designed to extend earlier promising work on important theoretical issues related to music processing. The research methods include behavioral measures and functional magnetic resonance imaging (fMRI). The project takes into account that simply knowing where an operation occurs in the brain is not necessarily useful. The present series of investigations, however, is designed in such a way that knowing where processing occurs in the brain may elucidate the more interesting questions of what is being processed in the brain, and how this processing occurs. Evidence from neuroanatomical studies can inform and constrain theories of cognition. Dr. Menon's project builds on his previous observation that Brodmann Area 47 in the Inferior Frontal Cortex (IFC) is responsible for processing structure in music, as well as spoken and sign language. This raises the intriguing possibility that this region instantiates a general purpose "temporal organization module." Accordingly, the specific aims of the proposed research are to: (1) extend his previous musical structure study with a new group of subjects so as to (a) obtain a more precise localization of temporal processing functions and (b) investigate similarities and differences in IFC and temporal lobe response and connectivity for familiar and unfamiliar music; (2) Manipulate the level of expectation present in the musical stimuli, in an effort to provide additional evidence for the syntactic operations subserved by the IFC; and (3) Examine the differential effects of variation in pitch and rhythm structure on brain responses in, and connectivity of, the IFC and the auditory cortex. Findings will be relevant to theories of modularity of mental function, temporal coherence, pattern perception, music cognition, and brain organization. This research will contribute to understanding the neural architecture supporting music and auditory perception and cognition in humans.

The broader impacts include strong involvement of students, including undergraduates, doctoral, and postdoctoral scholars. The project is multidisciplinary, bringing together two different groups of researchers, one trained in classical cognitive psychology, and the other trained in cognitive neuroscience and brain imaging.. Findings can help to better understand prefrontal and temporal cortex dysfunction in neurological and psychiatric disorders.

DESCRIPTION (provided by applicant): The maturation of mathematical reasoning skills is a hallmark of human cognitive development. Mathematical cognition, and more specifically, mental arithmetic (MA), provides a foundation for the development of analytical skills. Mathematical difficulties are widespread in school-age children and college students. Cognitive, developmental and educational psychologists have provided valuable insights into the complex and dynamic developmental changes in MA skills. However, little is known about the development of brain systems underlying these changes, and even less is known about the neural substrates of MA skill deficits in children with mathematical disabilities (MD). The overarching goal of our proposal is to investigate the neural basis of MD using state-of-the-art functional brain imaging techniques. The ability to quickly and accurately retrieve basic arithmetic facts is one of the most consistent deficits in children with MD. We therefore propose to investigate the neural basis of deficits in basic mental arithmetic (MA) operations, emphasizing the cognitive mechanisms differentially invoked by each operation. We will use a prospective longitudinal design to elucidate (i) the neural basis of deficits in MA skills, and (ii) the developmental trajectory of these deficits in children over a one year period between the 2nd grade (Time 1) and the 3rd grade (Time 2). For the first time, the proposed longitudinal study designs will allow us to assess (1) intra and inter-subject variability and stability of brain activation in relation to MA performance changes, and (2) specific classes of neurodevelopmental changes (poor vs. normal development). Our proposed studies will provide new insights into the neural basis of deficits in mathematical abilities, and how the increased recruitment of specialized MA processing networks with development is altered in children with MD. Our studies will also contribute new information on typical and atypical development of mathematical cognition, as well as the neural basis of individual differences in mathematical abilities. By providing essential knowledge about the neurobiological substrates of mathematical difficulties in children, and how they change with time, we will be able to inform the development of behavioral and educational strategies that may help improve children's mathematical skills at an early age.

[unreadable] DESCRIPTION (provided by applicant): Understanding the neural basis of human brain function requires knowledge about the spatial and temporal aspects of information processing. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) represent complementary brain imaging techniques in terms of their spatial and temporal resolution; hence, combining fMRI and EEG holds great promise for examining the spatial and temporal dynamics of sensory and cognitive processes underlying brain function. The main advantage of acquiring fMRI and EEG data in the same session is that the two types of data reflect the same neural process. However, a number of technical problems must be overcome before the benefits of this approach can be fully realized. In particular, EEG data acquired in the scanner is heavily contaminated by artifacts which can significantly reduce the quality of the data. This proposal brings together a strong multidisciplinary research team to solve major technical problems related to the acquisition, validation and analysis of simultaneous EEG and fMRI data. We propose to develop, test and validate novel procedures for artifact reduction in simultaneous EEG-fMRI acquisition at 3T. To achieve this goal, we will: (1) use computer simulations to compare the performance of our new artifact removal procedures with current procedures, (2) build a physical phantom to generate known current sources in the 3T magnet, and use it to validate and quantify the effectiveness of our procedures, and (3) use continuous, averaged and single-trial EEGs to demonstrate that our procedures can successfully recover task-relevant brain responses. The proposed studies will contribute important new information about optimal EEG-fMRI recording and analysis techniques, thereby helping to realize their full potential in human brain research. Findings from our study will also propel the development of new approaches to investigate the neural bases of psychiatric, neurological and neurodevelopmental disorders. [unreadable] [unreadable] [unreadable]

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The study of human brain function is arguably one of our society's most important endeavors in this new century. Although there has been an explosive amount of research in basic neurobiology, progress in understanding the function of large brain systems has been limited. Understanding the integrated functioning of the brain remains a significant scientific problem. Even "simple perception" involves many distributed brain regions, and discovering the network interactions among these regions is important for understanding a range of issues in neuroscience, psychology, neurology, and psychiatry. Essential for understanding human brain function is a detailed knowledge of the spatio-temporal dynamics of neuronal populations and their interactions during cognitive function.

The National Science Foundation will sponsor a conference to bring together a group of leading researchers to examine the dynamics of distributed brain function from a multidisciplinary approach, and to educate the next generation of researchers on important topics at the frontier of studies in this discipline. It will 1) present an overview of the present state of research on brain dynamics from various perspectives; 2) target issues in the brain sciences for which progress may be facilitated by the closer interaction of multiple disciplines; 3) promote the application of tools from mathematical statistics, network science, and neural network modeling; 4) outline avenues of approach to the application of insights from dynamical brain studies to clinical questions for the improved development of biomarkers for disease diagnosis. The conference will be held in California's San Francisco Bay area.

A fundamental question for learning theory is how people build upon a mature system of knowledge so they can go beyond that system. In an effort to develop a collaborative-from-the-start educational neuroscience, the PIs are addressing this question in the context of mathematics learning. By the age of twelve, children have nearly adult levels of fluency at comparing natural numbers. Child behavioral data and adult fMRI and behavioral data indicate that these comparisons depend on neurological processes that also compare perceptual magnitudes. Around this same age, children are introduced to the integers, which build upon, but go beyond, the natural numbers. The PIs' initial behavioral data indicate that children initially understand negative numbers by applying rules. For example, to determine the larger of a negative and a positive digit, they simply note which one has the negative sign. In contrast, the adult behavioral data exhibit the signature of a perceptual phenomenon known as categorical perception, such that zero has become an important boundary for making comparisons. Using a combination of fMRI, developmental, and instructional data, the PIs are testing whether the long-term combination of rules and the well-known natural numbers evolves into a representation of integers that partakes of perceptual processes. If so, this will provide a model instance of how educational neuroscience can contribute novel theories to education and neuroscience. For example, common wisdom has it that abstract rules arise from perceptual representations, but if the PIs' hypothesis is true, then the development of integer understanding is an instance where perception-like representations arise from the application of abstract rules.

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How do people develop well-structured representations for non-perceptual, quantitative concepts? For example, when mathematicians reason about five-dimensions, do they depend on internal spatial representations, do they apply a series of symbolic rules, or do they use a combination of both? The current research addresses this question in the context of children learning about zero and the negative numbers. By the time most children begin learning the integers, they have an internal spatial representation that supports their abilities to reason about natural number magnitude, even when presented symbolically as digits. Recent evidence, however, indicates that children?s reasoning with zero and negative numbers relies on the application of syntactic rules. This contrasts with most adults who have developed a spatial representation of negative numbers in their own right. A combination of instructional, behavioral, and fMRI methods are examining the relative influences of spatial and symbolic experiences on brain reorganization and children?s development of integer concepts.

The maturation of mathematical reasoning skills is a hallmark of human cognitive and academic development. Cognitive, developmental and educational psychologists have provided valuable insights into the complex and dynamic developmental changes in mathematical reasoning skills during childhood. The specific goal of our project is to investigate the development of mental arithmetic skills using a cognitive and systems neuroscience approach. This work has the potential to have broad impact on theories in cognitive neuroscience, brain and cognitive development and cognitive psychology. Findings from our proposed studies will inform the development of academic and educational programs for teaching mathematical and symbolic reasoning to children.

The maturation of mathematical reasoning skills is a hallmark of human cognitive and academic development. Cognitive, developmental, educational and clinical psychologists have provided valuable insights into the complex and dynamic developmental changes in mathematical skills during childhood. The ages between 7 and 10 (grades 2, 3 and 4) represent an important period for the development of mathematical skills. However, very little is currently known about the development of brain systems that mediate mathematical skill acquisition. The overarching goal of our proposal is to investigate the development of mathematical skills in children using a cognitive and systems neuroscience approach, and a longitudinal study design. The specific aims of this clinical research proposal are to (1) Investigate longitudinal changes in cognitive and brain processes mediating mathematical cognition at grade 4, (2) Investigate longitudinal changes in functional and structural connectivity of brain networks underlying mathematical cognition, and (3) Investigate the cognitive and neural basis of individual differences in mathematical skill development. Our proposed studies will provide important insights into (1) the neural basis of functional specialization for mathematical cognition;(2) the relation between neural changes and changes in working memory, strategy and increased task proficiency;(3) the developmental origins of functional specialization in the posterior parietal cortex;(4) cortical networks and interactions that subserve mathematical task performance at different stages of skill development;and (5) cognitive and neural factors that mediate individual differences in learning. Taken together, this work will provide significant new information on the maturation of brain networks important for mathematical cognition and skill acquisition. This work has the potential to have broad impact on developmental cognitive neuroscience, brain development, and clinical psychology.

DESCRIPTION (provided by applicant): Mathematical cognition provides a foundation for the development of skills that are indispensable for academic and professional success in the 21st century. Strong foundational knowledge in math is critical not only for success in the STEM fields but also as an important skill in everyday life. Poor numeracy is associated with negative and costly outcomes for health, well-being and life expectancy, making it a major public health concern. Mathematical difficulties are widespread in children, adolescents and even college students, and one in five adults in the US is functionally innumerate. Interventions for remediating poor math skills in children with mathematical disabilities (MD) have therefore taken on great significance. The long-term goal of our research is to understand the cognitive and brain mechanisms underlying mathematical learning, and remediation of poor math skills, in children with MD. Research into the mechanisms underlying interventions for assisting students struggling with math is critically needed, as emphasized by multiple expert panels. Our proposal seeks to extend a productive, innovative and high-impact line of research using a cognitive and systems neuroscience approach, together with state-of-the-art brain imaging techniques, to examine the mechanisms underlying remediation of mathematical skills in children with MD. Our proposed studies are highly relevant to the mission of the NIH Program Announcement Development of Mathematical Cognition and Reasoning and the Prevention of Math Learning Disabilities (PA-12-248). Building on our recent progress, in this renewal we now propose to investigate the cognitive and brain mechanisms underlying two important types of interventions that target different areas of weaknesses in children with MD - speeded practice tutoring (SPT), which targets fluent retrieval of math facts, and visuo-spatial number tutoring (VNT), which targets visuo-spatial representations of numbers, quantity, and their mental manipulations. We will use a randomized control design to compare these distinct learning approaches, and elucidate the brain mechanisms underlying short- and long-term learning, generalization (transfer), and retention of math skills associated with SPT and VNT in children with MD. Our central hypothesis is that SPT and VNT will remediate different types of math deficits in children with MD via dissociable patterns of brain plasticity. A critical neurobiological investigation of these two forms of learning will help elucidate the extent to which MD is remediated by interventions that target plasticity in dissociable brain systems - the declarative memory system, anchored in the medial temporal lobe and the visuo-spatial attention system, anchored in the intra- parietal sulcus. The proposed work will provide important new insights into the neurobiological basis of mathematical learning in children with MD and their typically developing peers. Findings from our study have major implications not only for informing the etiology and the remediation of MD but also for determining sources of variability in mathematical learning with broad consequences for optimizing learning in all children.

DESCRIPTION (provided by applicant): Mathematical cognition is critical not only for success in science and engineering but also as an important skill in everyday life, second only to reading in formal education. Yet, mathematical difficulties are widespread in school-age children and college students in the US. Understanding the progression and mechanisms of mathematical development is a national priority, as emphasized by the conclusions of the President's National Mathematics Advisory Panel. Recent cognitive, developmental and educational studies have provided new insights into the enduring behavioral deficits in children with mathematical disabilities (MD). However, little is known about the neural and anatomical bases of MD in children. The overarching objective of our proposal is to continue a productive line of research investigating cognitive and brain mechanisms underlying MD in young children. We will use a cognitive and systems neuroscience approach coupled with state-of-the-art functional magnetic resonance imaging (fMRI), structural MRI and diffusion tensor imaging (DTI) techniques to achieve this objective. Our study focuses on ages 7-10 (grades 2, 3 and 4), a period important for mastering core arithmetic skills that support later mathematics learning. A prospective longitudinal design will be used to elucidate the neural correlates of poor arithmetic skills in children with MD and examine why some children with MD have persistent deficits whereas others do not. Our proposed studies focus on three groups of children: (1) children with mathematical learning disabilities (MLD) who have persistent disabilities (low achievement across grades), (2) low achieving but variable (LA-V) children who lag in performance skills in one year and are normal the next, and (3) typically developing (TD) children. We will characterize the behavioral, cognitive and neural profile of information processing deficits during addition and subtraction, two basic and complementary arithmetic operations that differ in task complexity and efficient retrieval. Analysis of DTI and fMRI data acquired from the same children will contribute important new knowledge about core neuroanatomical deficits in persistent MD. Novel multivariate pattern recognition techniques, which detect fine-grained differences in activation patterns, will be used to increase our ability to uncover aberrant neural representations of mathematical information in children with MD. The longitudinal study design will allow us to (1) assess intra- and inter-subject variability and stability of brain response and connectivity in relation to arithmetic skill development, and (2) identify latent classes of neurodevelopmental changes characterizing poor and normal development. Our proposed studies will provide new insights into the neural correlates of MD, and the extent to which increased recruitment of brain networks involved in arithmetic processing during development is altered in children with MD. By providing essential knowledge about the neurofunctional and neuroanatomical substrates of MD in children, and how they change with time, we will be able to inform the development of behavioral and educational strategies for improving mathematical skills at an early age. PUBLIC HEALTH RELEVANCE: Understanding the progression and mechanisms of mathematical development mathematical skills is a national priority, as emphasized by the formation of the President's National Mathematics Panel. Between 5 to 8% of children demonstrate some form of mathematical learning disability, with adverse life-long consequences for academic, vocational and professional success. The overarching objective of our proposal is to continue a productive line of research investigating the cognitive and brain mechanisms underlying MD in young children. Findings from our study will not only have important implications for determining mathematical learning in children, but also for understanding the cognitive and brain processes underlying mathematical learning disabilities.

DESCRIPTION (provided by applicant): Impaired phonological processing and abnormal perception of human voice are two critical, yet understudied, aspects of language and social impairments in children with autism spectrum disorders (ASD). Despite the prevalence and adverse impact of these deficits, the brain mechanisms underlying these phenomena have received surprisingly little experimental investigation, particularly in children with ASD. The primary goal of the proposed research is to further our understanding of basic auditory function underlying decoding of phonological content ("what" is being said) and speaker identity ("who" is saying it) in children with ASD, compared to typically developing (TD) children matched on age and language ability. To achieve this goal, we propose three experiments in which novel multivariate pattern recognition techniques will be used to increase the sensitivity for detecting fine-grained neural representations of information processing in the auditory system of children with ASD. In the first experiment, functional MRI will be used to examine discrimination of minimal pair nonsense words in auditory cortex of children with high-functioning autism (HFA) and TD children. Next, we will assess the integrity of voice-selective cortex in the superior temporal sulcus of children with HFA and TD children by quantifying brain-based discrimination of speech and non-speech environmental sounds measured with functional MRI. In the third experiment, we will use functional MRI to distinguish brain responses to words produced by each child's mother and investigate specialized brain circuits for typical and atypical processing of this salient biological signal. Findings from these experiments will provide novel information about the integrity of acoustical and phonological processing of mother's and unfamiliar voice in children with ASD, knowledge that is essential for a more complete characterization of the pervasive language deficits in autism. The proposed studies are also innovative in that they will use novel multivariate pattern recognition techniques to increase the sensitivity for detecting fine-grained neural representations of auditory information processing in children with ASD. Our research has the potential to not only provide new information about impaired speech and voice processing in autism but also, more generally, to push methodological boundaries for studying neurodevelopmental disorders. PUBLIC HEALTH RELEVANCE: The long-term goal of our research is to further our understanding of basic auditory function in individuals with autism. The primary goal of the proposed work is to better understand the neural basis of auditory information processing deficits in children with autism. Children with autism often exhibit receptive language impairments, including atypical processing of phonemes and voices, including their own mother's voice. Our goal is to better understand the neural bases of these phenomena using multivariate pattern recognition techniques applied to functional magnetic resonance imaging. A better understanding of neurobiological and behavioral bases of phonological disorders and voice recognition will provide novel insights into language deficits in autism.

DESCRIPTION (provided by applicant): Cognitive information processing depends on dynamical interactions between distributed brain areas. In the past decade, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for investigating human brain function. Although fMRI research has primarily focused on identifying brain regions that are activated during performance of cognitive tasks, there is growing consensus that cognitive functions emerge as a result of dynamic, context-dependent, causal interactions between multiple brain areas. Devising and validating methods for investigating such interactions has therefore taken added significance. Despite the growing need, the accuracy of current methods for identifying causal interactions in fMRI data remain poorly understood. The overall goal of this proposal is to address a critical need in fMRI by developing and testing new algorithms and software for identifying context-dependent causal interactions between distributed brain regions. We will first develop and validate novel methods based on a Multivariate Dynamical Systems (MDS) framework that overcomes several limitations of existing methods. We will then compare the performance of our new methods with other methods on both simulated and real fMRI data. Important contributions of these proposed studies include (1) development of novel multivariate state space methods for estimating causal interactions between brain regions and (2) first and most detailed evaluation of not only MDS but also other effective connectivity methods using both simulated and experimental fMRI data. Together, these studies will lead to new and improved tools for analyzing functional brain connectivity using fMRI. More generally, our proposed methods will help to advance knowledge of the dynamical basis of human cognitive function and will provide new tools for investigating neurodevelopmental, psychiatric and neurological disorders such as autism, schizophrenia and Parkinson's disease. The proposed studies are highly relevant to the mission of the NIH Exploratory Innovations in Biomedical Computational Science and Technology Program Announcement (PA 09-219), which seeks to encourage development of innovative advanced computational tools for brain imaging.

DESCRIPTION (provided by applicant): Autism Spectrum Disorder (ASD) is a heterogeneous disorder characterized by specific behavioral deficits. However, its altered developmental trajectory can also lead to cognitive strengths, particularly in the domains of mathematical and analytical problem solving. Mathematical cognition is critical not only for success in science and engineering but also as an important skill in everyday life, second only to reading. Despite its importance, numerical and mathematical reasoning is a grossly understudied cognitive domain in ASD. Here, we propose to initiate the first systematic study of mathematical cognition in children with ASD, focusing initially on children with High Functioning Autism (HFA). The overarching goal of this proposal is to investigate number sense, mathematical reasoning and problem solving abilities in children with HFA using a cognitive and systems neuroscience approach. The specific aims of this project are: (1) To behaviorally characterize mathematical abilities in children with ASD using a battery of standardized and novel cognitive tests, (2) To investigate the cognitive and brain processes underlying basic number sense in children with ASD and compare them to typically developing (TD) children, (3) To investigate the cognitive and brain processes underlying arithmetic problem solving abilities in children with ASD and compare them to TD children, and (4) To examine the integrity of functional and structural networks supporting mathematical cognition in children with ASD compared to TD children. We will test the hypotheses that (i) mathematical cognition is an islet of relative ability in some children with ASD and (ii) children with ASD, whether they exhibit superior, equivalent, or poorer performance levels as TD controls, will deploy atypical brain processes during numerical and mathematical problem solving. Our proposed studies will provide new insights into the neural basis of mathematical abilities in children with ASD, and the extent to which brain networks supporting mathematical information processing are altered in these children. If, as we predict, mathematical skills are an islet of ability in some children with ASD, the proposed studies will provide novel insights into the neurobiological mechanisms underlying cognitive and behavioral heterogeneity in children with ASD. Our research will not only lead to a more thorough understanding of the neural systems mediating heterogeneity of cognitive functioning and problem solving abilities in ASD, but it will also have important implications for designing appropriate interventions to improve academic, vocational and professional achievement in individuals with ASD by identifying their unique strengths and weaknesses at an early age.

DESCRIPTION (provided by applicant): Impaired phonological processing and abnormal perception of human voice are two critical, yet understudied, aspects of language and social impairments in children with autism spectrum disorders (ASD). Despite the prevalence and adverse impact of these deficits, the brain mechanisms underlying these phenomena have received surprisingly little experimental investigation, particularly in children with ASD. The primary goal of the proposed research is to further our understanding of basic auditory function underlying decoding of phonological content (what is being said) and speaker identity (who is saying it) in children with ASD, compared to typically developing (TD) children matched on age and language ability. To achieve this goal, we propose three experiments in which novel multivariate pattern recognition techniques will be used to increase the sensitivity for detecting fine-grained neural representations of information processing in the auditory system of children with ASD. In the first experiment, functional MRI will be used to examine discrimination of minimal pair nonsense words in auditory cortex of children with high-functioning autism (HFA) and TD children. Next, we will assess the integrity of voice-selective cortex in the superior temporal sulcus of children with HFA and TD children by quantifying brain-based discrimination of speech and non-speech environmental sounds measured with functional MRI. In the third experiment, we will use functional MRI to distinguish brain responses to words produced by each child's mother and investigate specialized brain circuits for typical and atypical processing of this salient biological signal. Findings from these experiments will provide novel information about the integrity of acoustical and phonological processing of mother's and unfamiliar voice in children with ASD, knowledge that is essential for a more complete characterization of the pervasive language deficits in autism. The proposed studies are also innovative in that they will use novel multivariate pattern recognition techniques to increase the sensitivity for detecting fine-grained neural representations of auditory information processing in children with ASD. Our research has the potential to not only provide new information about impaired speech and voice processing in autism but also, more generally, to push methodological boundaries for studying neurodevelopmental disorders. PUBLIC HEALTH RELEVANCE: The long-term goal of our research is to further our understanding of basic auditory function in individuals with autism. The primary goal of the proposed work is to better understand the neural basis of auditory information processing deficits in children with autism. Children with autism often exhibit receptive language impairments, including atypical processing of phonemes and voices, including their own mother's voice. Our goal is to better understand the neural bases of these phenomena using multivariate pattern recognition techniques applied to functional magnetic resonance imaging. A better understanding of neurobiological and behavioral bases of phonological disorders and voice recognition will provide novel insights into language deficits in autism.

DESCRIPTION (provided by applicant): In the past two decades, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for investigating human brain function. Although fMRI research has primarily focused on identifying brain regions that are activated during performance of cognitive tasks, there is growing interest in examining how cognitive functions emerge as a result of context-dependent, dynamic causal interactions between distributed brain regions. Devising and validating methods for investigating such interactions has therefore taken on great significance. The first major goal of this proposal is to address a critical need in fMRI research by developing novel algorithms for identifying context-dependent dynamic causal interactions between distributed brain regions. To this end, we will develop and validate novel computational methods using Multivariate Dynamical Systems based Markov chain Monte Carlo (MDS-MCMC) algorithms that overcome major limitations of existing methods for investigating dynamic causal interactions and connectivity in the human brain. A comprehensive validation framework will be use evaluate MDS-MCMC and compare it with existing dynamic causal estimation methods. The second major goal of this proposal is to use the MDS-MCMC framework to investigate dynamic causal interactions underlying cognition in normal healthy adults, and in patients with Parkinson's disease (PD). Cognitive impairment is one of the most devastating symptoms in PD. Once thought of as an insignificant feature of the disease, it is now clear that cognitive impairment is present in the majority of PD patients and that this impairment is significantly linked to increased disability and the risk of mortality, yetlittle is known about the brain basis of cognitive impairment in PD. The computational algorithms we develop, validate, and apply here will allow us to rigorously investigate brain dynamics support critical cognitive processes in the human brain, leading to a more complete understanding of fundamental mechanisms underlying human brain function and dysfunction. Our proposed studies will also, for the first time, examine casual interactions in simulated, open-source, opto-genetic, experimental and clinical brain imaging data using state-of-the-art sub-second high-temporal resolution fMRI, based on the Human Connectome Project (HCP). Critically, we will maintain a tight link between our computational and systems neuroscience goals algorithms to solve important problems in cognitive, systems and clinical neuroscience. Together, our proposed studies will lead to new and improved computational tools for examining dynamical causal interactions between distributed brain regions, with broad applications to the HCP and clinical neuroscience. The proposed studies are highly relevant to the mission of the NIH Innovations in Biomedical Computational Science and Technology and the Big Data to Knowledge Programs, which seek to encourage development and dissemination of innovative advanced computational tools for brain imaging and neuroscience. We will disseminate our algorithms and software to the research community via NITRC .

Project Summary/Abstract Understanding how the human brain produces cognition ultimately depends on precise quantitative characterization of context-dependent dynamic functional networks (DFN) that transiently link distributed brain regions. Progress in achieving this goal has been limited due to a lack of theoretical frameworks for characterizing DFNs and appropriate computational methods to test them. Devising and validating computational methods for investigating DFNs in the human brain is thus of great significance. The first major goal of this proposal is to address a critical need in human brain research by developing novel algorithms for identifying DFNs and characterizing dynamic network interactions between distributed brain regions. To achieve this goal, we will develop and validate novel computational methods within the framework of Bayesian switching linear dynamical systems (BSDS) with vector autoregressive models (VAR) and factor analysis (FA) that overcome major limitations of existing methods for investigating dynamic interactions in the human brain. The second major goal of this proposal is to use BSDS to investigate DFNs underlying cognitive function in healthy adults, and in patients with Parkinson's disease (PD). Severe cognitive impairment is one of the most devastating behavioral outcomes in patients with PD, yet little is known about the temporal properties of dysfunctional neurocognitive systems in this debilitating disorder. The computational algorithms we propose to develop, validate, and apply will allow us to rigorously investigate brain dynamics that support critical cognitive functions and significantly advance our understanding of dynamic processes underlying human brain function and dysfunction. Our proposed studies will also, for the first time, investigate DFNs in simulated, rodent in vivo optogenetic fMRI, as well as human data using state-of-the-art (sub- second) high-temporal resolution fMRI data generated by the NIH-funded Stanford Alzheimer's Disease Research Center (ADRC), highlighting critical translational applications of our proposed methods. Our proposed studies will provide novel tools for investigating dynamic functional networks in the human brain, with innovative applications to the Human Connectome Project (HCP) and the study of neurological disorders and clinical neuroscience more broadly. The proposed studies are highly relevant to the mission of the BRAIN Initiative (RFA-EB-15-006), which calls for the development and dissemination of innovative computational tools for probing human brain function and dysfunction. Our computational tools will be widely disseminated to facilitate research into the dynamical aspects of human brain function.

Learning disabilities during childhood have adverse long-term consequences for academic and professional success and quality of life. Impaired learning can be particularly detrimental for children with autism spectrum disorder (ASD). Relative to their peers, children with ASD go on to achieve lower levels of post-secondary education, employment, and independent living. Yet, the cognitive, behavioral, and neural mechanisms underlying learning in children with ASD remain poorly understood. ASD is characterized by heterogeneous clinical presentations and cognitive abilities, likely resulting in highly variable learning profiles in affected children. Little is known about the neurobiology of learning in school-age children with ASD in any cognitive domain. In the current project period, we found heterogeneous patterns of math abilities in a large cohort of children with ASD, consistent with epidemiological reports indicating that 17-40% of children with ASD display lower-than-expected math achievement scores. We also found novel evidence that math abilities are associated with restricted and repetitive interests and behaviors (RRIB), a core clinical symptom of ASD. We propose, in this renewal, to leverage our innovative and high-impact line of research to investigate heterogeneity in learning and brain plasticity, and its links to RRIB and cognitive inflexibility, in children with ASD. Using a theoretically motivated cognitive training protocol, state-of-the-art brain imaging, and advanced multivariate computational techniques, we propose to test the hypotheses that (i) children with ASD and low math abilities (LMA-ASD) will show different learning profiles relative to children with ASD and high math abilities (HMA-ASD) and typically developing (TD) children and (ii) compared to the HMA-ASD and TD groups, the LMA-ASD group will demonstrate weaker plasticity in two distinct brain systems important for math learning: the visuospatial number system, anchored in the intra-parietal sulcus and fusiform gyrus, and the declarative memory system, anchored in the medial temporal lobe. We will also investigate whether RRIB and cognitive inflexibility have a negative influence on learning in ASD, and determine the extent to which these effects are mediated by aberrant functioning of the salience network, a prefrontal cognitive control system anchored in the anterior insula and anterior cingulate cortex. The proposed work will provide important new insights into the neurocognitive basis of heterogeneous learning profiles in children with ASD and is highly relevant to the mission of the NIH ?Research on Autism Spectrum Disorders? (PA-16-388). Identifying behavioral and neural sources of heterogeneity in learning and their links to clinical symptoms in a quantitatively rigorous manner will have significant implications for informing the etiology of ASD and more critically, for optimizing learning in affected children.

Project Summary/Abstract Mathematical cognition provides a foundation for the development of quantitative skills critical for functioning in the 21st century. Yet, math difficulties are widespread in children, adolescents, and college students, and one in five adults in the USA is functionally innumerate. Low numeracy is associated with poorer health outcomes, reduced health literacy, and improper use of health resources. Characterizing neurocognitive developmental trajectories and risk factors of mathematical disabilities (MD) is critical for addressing the public health burdens of innumeracy. Building on an innovative and high-impact line of research, we propose to investigate neurocognitive longitudinal trajectories and outcomes in MD. We focus on two key cognitive domains impaired in MD: (1) number sense, including representations of quantities, numbers, and their mental manipulation, and (2) arithmetic skills, including numerical problem solving and fluent retrieval of math facts from memory. Our central hypothesis is that, relative to typically developing (TD) controls, individuals with MD will exhibit atypical developmental trajectories of brain response, representations, and connectivity in two functional brain systems: (1) the parietal visuo-spatial attention system, which supports quantity representations, and (2) the medial temporal (MTL) declarative memory system, which supports arithmetic fact retrieval. We will test (1) core and access deficit models of atypical number sense development and (2) a memory deficit model of weak fact retrieval. Using state-of-the-art multimodal brain imaging and three innovative longitudinal designs, we will test these models by (1) characterizing developmental trajectories in children and adolescents, spanning elementary, middle, and high school years (ages 7 to 16), with an accelerated longitudinal design; (2) identify brain measures that predict longitudinal 2-year early math trajectories and outcomes in young children prior to formal instruction or MD diagnosis (ages 5-7); and (3) identify brain measures that predict longitudinal 10-year long-term math outcomes in adolescents (age 17) who were previously characterized in childhood. Three innovative longitudinal designs will address critical gaps in our understanding of neurocognitive systems impacted over development in MD. Findings will inform our understanding of the etiology of MD and the development of targeted cognitive interventions that may ultimately reduce the public health burden of low numeracy.

Project Abstract Impairments in cognitive systems that regulate the ability to adaptively engage with and respond to changing stimuli and goals are a hallmark of psychopathology. Identifying the underlying cognitive and neural factors that drive dysfunctional behavioral dynamics is a primary goal for psychiatric research. However conventional methods are unable to reveal latent constructs that govern these dynamic processes. Novel computational approaches are required to reveal latent behavioral dynamics and traits associated with psychopathology, and their neural circuit basis, within the Research Domain Criteria (RDoC) framework. Most, if not all, psychiatric disorders have a neurodevelopmental origin and are associated with atypical maturation of cognitive brain networks. Cognition is a dynamic process, which relies on flexible inhibitory control, goal-directed beliefs that impact moment-to-moment expectation, and the capacity to learn and adapt from prior decisions. Developing dynamic latent behavioral models of cognition is significant in the context of psychopathology, because deficits in inhibitory control, performance monitoring and belief updating are implicated in multiple psychiatric disorders including ADHD, autism, and schizophrenia. Our overarching goal is to develop and validate Hierarchical Latent Variable Dynamics (HLVD), a novel integrative computational approach for discovering robust latent behavioral constructs and their neural circuit bases. The proposed studies will leverage the longitudinal Adolescent Behavioral and Cognitive Development (ABCD) study, which has generated unprecedented amounts of ?Big Data? (N>5,000) for charting cognitive and brain development in children and adolescents over time. Crucially, HLVD will be used to identify and validate novel latent constructs of behavioral dynamics that are expected to be significant dimensional predictors of externalizing symptoms and developmental psychopathology. The proposed studies will significantly enhance our understanding of RDoC constructs and provide new insights into latent behavioral dynamics and traits associated with psychopathology in the developing brain. Our studies are highly relevant to the mission of the NIMH initiative RFA-MH-19-242, which seeks to accelerate research on neurodevelopment and trajectories of risk for mental illness. Our innovative approach will ultimately aid in the development of biomarkers for early detection and treatment of psychiatric disorders.

The goal of this project, led by a team of researchers at Stanford University, is to develop novel neurocognitive models that integrate behavioral and neural (fMRI, functional magnetic resonance imaging) data to understand the computational, cognitive, and brain mechanisms underlying individual differences in mathematical problem solving and strategy use, the effects of training, and longitudinal development. Understanding how symbols are processed in the brain has direct implications for education and the remediation of cognitive difficulties. The researchers will perform sophisticated computational analyses on a dataset derived from children 7 to 12 years of age in an fMRI study, a tutoring study, and a longitudinal study. The findings of this study will provide a basis for customized training and will provide novel platforms for diagnostic and intervention procedures for learning difficulties. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

This research will leverage extensive behavioral data, cognitive assessments, as well as brain imaging data, to model moment-by-moment changes in latent cognitive dynamics associated with mathematical problem solving. The multidisciplinary approach described here seeks to develop unsupervised computational models to infer differences in the latent cognitive strategies used by an individual on a trial-by-trial basis. Specifically, this project aims to (1) develop computational cognitive models for inferring latent problem-solving dynamics and strategy use, (2) develop novel integrated neurocognitive models to identify distinct and overlapping brain circuits underlying latent problem-solving dynamics and strategy use, and (3) determine integrated cognitive and neural mechanisms underlying the impact of cognitive tutoring on changes in latent problem-solving dynamics and strategy use. The planned studies will help in dissociating mathematical problem solving into multiple cognitive sub-processes, characterizing sources of individual differences across these sub-processes, identifying how each of these might relate to difficulties in problem solving, and evaluate whether these cognitive sub-processes may be remediated by cognitive tutoring programs. Ultimately, this research will enhance our understanding of dynamic cognitive processes and problem-solving strategies in children?s numerical cognition and provide new insights into latent behavioral dynamics associated with typical and atypical math abilities in children.

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.

Project Abstract Speech communication impairments, including persistent difficulties in understanding and interpreting verbal information during conversation, are a hallmark of childhood autism. Speech-based communication unfolds over time, and speech comprehension relies on: (1) anticipation of incoming speech as a means of predicting its content and (2) temporal integration of speech so a listener can accumulate information over time to decode meaning in an extended utterance. Prominent theories of autism spectrum disorder (ASD) posit deficits in contextual and global information processing, which are germane to the anticipation and integration of information during communication. Late childhood is a crucial period for increased and more complex social interactions, including extended discourse between communication partners. Little is known regarding anticipatory and integrative components of speech processing, and their contribution to social communication (SC) deficits, in children with ASD. Advances in experimental design and computational analysis of human brain imaging data provide a unique opportunity to probe dynamic components of speech comprehension during naturalistic social interactions in children with ASD, which are difficult to ascertain using behavioral methods alone. Leveraging innovative fMRI experimental designs, we will for the first time investigate anticipatory and integrative aspects of naturalistic communication in children with ASD. Our overarching goal is to identify the neurocognitive mechanisms underlying speech comprehension deficits during naturalistic communication in children with ASD. The proposed studies include both speaker-listener brain coupling and temporal integration paradigms and build on our high-impact line of voice perception research in children with ASD. We hypothesize that children with ASD will show deficits in dynamic mechanisms of speech comprehension including anticipatory, reactive, and integrative processing with dissociable patterns of dysfunction in the default mode network (DMN), anchored in medial prefrontal cortex (mPFC) and posterior medial cortex (PMC), and lateral frontoparietal network (LFPN). While the DMN is often considered a ?task- negative? network, evidence shows that the DMN is crucial for processing social information, including narrative processing, and is closely linked to SC deficits in ASD. We hypothesize a link between these dynamic mechanisms and comprehension of global, but not local, narrative information, supporting the Weak Central Coherence model of ASD. Findings will provide new insights into speech comprehension impairments and advance our understanding of the role of the DMN in SC and ASD. Our studies will provide critical information regarding the neurobiological origins of communication impairments in ASD and will inform the development of age-appropriate treatment for older children with ASD. Our aims are in line with the NIH directive on Autism Research (PA-18-400), emphasizing brain mechanisms and sophisticated measures of social communication.

Project Abstract Alzheimer?s disease (AD) is a progressive and severely debilitating disease that negatively affects cognitive and memory function and is linked to increased disability in everyday functioning and risk of mortality. Language function is a crucial area of impairment for AD. Individuals with AD show a wide range of language difficulties which are thought to lead to social isolation in affected individuals, negatively affecting quality of life and well-being for patients, caregivers, and family members. The major goal of our Administrative Supplement is to advance our understanding of the brain mechanisms underlying speech comprehension deficits in patients with AD. To accomplish this goal, we will leverage fMRI task paradigms and neurocognitive models we have recently developed as part of the parent project and ongoing work in collaboration with the Stanford Alzheimer?s Disease Research Center (ADRC). We now propose to extend our original aims with two new Aims designed to build on these findings by examining anticipatory and integrative components of speech processing, with a focus on aberrant organization of the default mode network, in patients with early AD. Critically, to achieve this goal, we will apply the novel experimental and analytic approaches developed by the parent project at Stanford to AD data collected as part of the proposed supplement and resources provided by the Stanford ADRC (P30AG066515) Clinical Core and Imaging Core. Specifically, we will investigate (1) speaker-listener brain coupling during natural speech communication and its relation to narrative comprehension and functional communication abilities in AD and age-matched healthy controls; and (2) the integrity of temporal integration windows underlying naturalistic verbal communication and its relation to narrative comprehension and functional communication abilities in AD and age-matched healthy controls. Our studies will provide critical information regarding the neurobiological origins of communication impairments in AD and will inform therapies and strategies aimed at improving language and social function in dementia.

Project Abstract Alzheimer?s disease (AD) affects over 5.7 million Americans and is expected to rise to nearly 14 million people by 2060, as the number of people living with this disease doubles every 5 years. AD is a progressive and severely debilitating disease that negatively affects cognitive and memory function and is linked to increased disability in everyday functioning and risk of mortality. Neurodegeneration of focal brain areas in AD progressively impacts large-scale brain circuits, leading to significant cognitive and behavioral impairments. However, little is known regarding aberrant context-dependent dynamic causal interactions between distributed brain regions, and their links to cognitive and memory impairments and neuropathology, across AD clinical stages. Leveraging a productive and high-impact line of research in the current project period, we now propose to address critical gaps in our knowledge of functional circuit mechanisms underlying cognitive dysfunction in AD using innovative computational tools. Our first major goal is to continue to address critical unmet needs in human brain research by developing and validating novel computational tools for identifying context-dependent dynamic causal interactions between distributed brain regions. Building on progress in the current project period, we will further develop novel Multivariate Dynamic Systems Identification-Hamiltonian Monte Carlo techniques taking advantage of recent advances in Bayesian modeling and inferencing. Our computational tools will be validated using optogenetic stimulation with whole-brain fMRI, and stability analysis of normative Human Connectome Project data. Our second major goal is to use MDSI-HMC to investigate aberrancies in dynamic causal circuits underlying cognitive and memory impairment in AD. Our system neuroscience approach will target four key brain systems implicated in AD: default mode network, medial temporal lobe, and two frontal control systems anchored in the frontoparietal and salience networks. To achieve our goals, we will leverage clinical, phenotypic, cognitive, experimental, and state-of-the-art fMRI, and beta amyloid (A?) and tau PET, data from multiple NIH-funded AD-specific Human Connectome Projects. Our proposed studies will advance foundational knowledge of cognitive and memory-related circuits across AD clinical stages and their links to neuropathology. More generally, our proposed studies will also contribute novel tools for examining dynamical causal circuits underlying human brain function and dysfunction. The proposed studies are highly relevant to the NIH Focus on AD and PAR- 10-070 which call for innovative characterization of functional brain circuits altered in AD. More broadly, the proposed studies are relevant to the mission of the NIH to encourage development and dissemination of innovative advanced computational tools for clinical neuroscience. We will disseminate our algorithms and software tools to the research community as we have done in the current project period.