Vinod Menon

Stanford Medicine, Stanford, California, United States

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Publications (198)1313.02 Total impact

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    ABSTRACT: Despite reports of mathematical talent in autism spectrum disorders (ASD), little is known about basic number processing abilities in affected children. We investigated number sense, the ability to rapidly assess quantity information, in 36 children with ASD and 61 typically developing controls. Numerical acuity was assessed using symbolic (Arabic numerals) as well as non-symbolic (dot array) formats. We found significant impairments in non-symbolic acuity in children with ASD, but symbolic acuity was intact. Symbolic acuity mediated the relationship between non-symbolic acuity and mathematical abilities only in children with ASD, indicating a distinctive role for symbolic number sense in the acquisition of mathematical proficiency in this group. Our findings suggest that symbolic systems may help children with ASD organize imprecise information.
    Full-text · Article · Dec 2015 · Journal of Autism and Developmental Disorders
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    ABSTRACT: Objective: Cognitive impairments in Parkinson's disease (PD) are thought to be caused in part by dopamine dysregulation. However, even when nigrostriatal dopamine neuron loss is severe enough to cause motor symptoms, many patients remain cognitively unimpaired. It is unclear what brain mechanisms allow these patients to remain cognitively unimpaired despite substantial dopamine dysregulation. Methods: 31 cognitively unimpaired PD participants OFF dopaminergic-medications were scanned using fMRI while they performed a working memory task, along with 23 controls. We first compared the PD_OFF medication group with controls to determine whether PD participants engage compensatory frontostriatal mechanisms during working memory. We then studied the same PD participants ON dopaminergic-medications to determine whether these compensatory brain changes are altered with dopamine. Results: Controls and PD showed working memory load-dependent activation in the bilateral putamen, anterior-dorsal insula, supplementary motor area, and anterior cingulate cortex. Compared to controls, PD_OFF showed compensatory hyper-activation of bilateral putamen and posterior insula, and machine learning algorithms identified robust differences in putamen activation patterns. Compared to PD_OFF, PD_ON showed reduced compensatory activation in the putamen. Loss of compensatory hyper-activation ON dopaminergic-medication correlated with slower performance on the working memory task and slower cognitive speed on the Symbol Digit Modality Test. Interpretation: Our results provide novel evidence that PD patients maintain normal cognitive performance through compensatory hyper-activation of the putamen. Dopaminergic-medication down-regulates this hyper-activation and the degree of down-regulation predicts behavior. Identifying cognitive compensatory mechanisms in PD is important for understanding how some patients maintain intact cognitive performance, despite nigrostriatal dopamine loss. This article is protected by copyright. All rights reserved.
    Preview · Article · Dec 2015 · Annals of Neurology
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    ABSTRACT: Human cognitive problem solving skills undergo complex experience-dependent changes from childhood to adulthood, yet most neurodevelopmental research has focused on linear changes with age. Here we challenge this limited view, and investigate spatially heterogeneous and nonlinear neurodevelopmental profiles between childhood, adolescence, and young adulthood, focusing on three cytoarchitectonically distinct posterior parietal cortex (PPC) regions implicated in numerical problem solving: intraparietal sulcus (IPS), angular gyrus (AG), and supramarginal gyrus (SMG). Adolescents demonstrated better behavioral performance relative to children, but their performance was equivalent to that of adults. However, all three groups differed significantly in their profile of activation and connectivity across the PPC subdivisions. Activation in bilateral ventral IPS subdivision IPS-hIP1, along with adjoining anterior AG subdivision, AG-PGa, and the posterior SMG subdivision, SMG-PFm, increased linearly with age whereas the posterior AG subdivision, AG-PGp, was equally deactivated in all three groups. In contrast, the left anterior SMG subdivision, SMG-PF showed an inverted U-shaped profile across age groups such that adolescents exhibited greater activation than both children and young adults. Critically, greater SMG-PF activation was correlated with task performance only in adolescents. Furthermore, adolescents showed greater task-related functional connectivity of the SMG-PF with ventro-temporal, anterior temporal and prefrontal cortices, relative to both children and adults. These results suggest that nonlinear up-regulation of SMG-PF and its interconnected functional circuits facilitate adult-level performance in adolescents. Our study provides novel insights into heterogeneous age-related maturation of the PPC underlying cognitive skill acquisition, and further demonstrates how anatomically precise analysis of both linear and nonlinear neurofunctional changes with age is necessary for more fully characterizing cognitive development.
    No preview · Article · Dec 2015 · NeuroImage
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    ABSTRACT: Background: Attention-deficit/hyperactivity disorder (ADHD) is increasingly viewed as a disorder stemming from disturbances in large-scale brain networks, yet the exact nature of these impairments in affected children is poorly understood. We investigated a saliency-based triple-network model and tested the hypothesis that cross-network interactions between the salience network (SN), central executive network, and default mode network are dysregulated in children with ADHD. We also determined whether network dysregulation measures can differentiate children with ADHD from control subjects across multisite datasets and predict clinical symptoms. Methods: Functional magnetic resonance imaging data from 180 children with ADHD and control subjects from three sites in the ADHD-200 database were selected using case-control design. We investigated between-group differences in resource allocation index (RAI) (a measure of SN-centered triple network interactions), relation between RAI and ADHD symptoms, and performance of multivariate classifiers built to differentiate children with ADHD from control subjects. Results: RAI was significantly lower in children with ADHD than in control subjects. Severity of inattention symptoms was correlated with RAI. Remarkably, these findings were replicated in three independent datasets. Multivariate classifiers based on cross-network coupling measures differentiated children with ADHD from control subjects with high classification rates (72% to 83%) for each dataset. A novel cross-site classifier based on training data from one site accurately (62% to 82%) differentiated children with ADHD on test data from the two other sites. Conclusions: Aberrant cross-network interactions between SN, central executive network, and default mode network are a reproducible feature of childhood ADHD. The triple-network model provides a novel, replicable, and parsimonious systems neuroscience framework for characterizing childhood ADHD and predicting clinical symptoms in affected children.
    No preview · Article · Nov 2015 · Biological psychiatry
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    ABSTRACT: Autism spectrum disorder (ASD) is characterized by reduced attention to salient social stimuli. Here we use two visual oddball tasks to investigate brain systems engaged during attention to social (face) and non-social (scene) stimuli. We focused on the dorsal and ventral subdivisions of the anterior insula (dAI and vAI, respectively), anatomically distinct regions contributing to a ‘salience network’ that is known to regulate attention to behaviorally meaningful stimuli. Children with ASD performed comparably to their typically developing (TD) peers, but they engaged the right dAI and vAI differently in response to deviant faces compared with deviant scenes. Multivariate activation patterns in the dAI reliably discriminated between children with ASD and TD children with 85% classification accuracy, and children with ASD activated the vAI more than their TD peers. Children with ASD and their TD peers also differed in dAI connectivity patterns to deviant faces, with stronger within-salience network interactions in the ASD group and stronger cross-network interactions in the TD group. Our findings point to atypical patterns of right anterior insula activation and connectivity in ASD and suggest that multiple functions subserved by the insula, including attention and affective processing of salient social stimuli, are aberrant in children with the disorder.
    No preview · Article · Oct 2015 · Social Cognitive and Affective Neuroscience
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    ABSTRACT: Competency with numbers is essential in today's society; yet, up to 20% of children exhibit moderate to severe mathematical learning disabilities (MLD). Behavioural intervention can be effective, but the neurobiological mechanisms underlying successful intervention are unknown. Here we demonstrate that eight weeks of 1:1 cognitive tutoring not only remediates poor performance in children with MLD, but also induces widespread changes in brain activity. Neuroplasticity manifests as normalization of aberrant functional responses in a distributed network of parietal, prefrontal and ventral temporal-occipital areas that support successful numerical problem solving, and is correlated with performance gains. Remarkably, machine learning algorithms show that brain activity patterns in children with MLD are significantly discriminable from neurotypical peers before, but not after, tutoring, suggesting that behavioural gains are not due to compensatory mechanisms. Our study identifies functional brain mechanisms underlying effective intervention in children with MLD and provides novel metrics for assessing response to intervention.
    Full-text · Article · Sep 2015 · Nature Communications
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    ABSTRACT: Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals.
    Full-text · Article · Sep 2015 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
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    Kaustubh Supekar · Vinod Menon
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    ABSTRACT: Background: Autism spectrum disorder (ASD) is diagnosed much less often in females than males. Emerging behavioral accounts suggest that the clinical presentation of autism is different in females and males, yet research examining sex differences in core symptoms of autism in affected children has been limited. Additionally, to date, there have been no systematic attempts to characterize neuroanatomical differences underlying the distinct behavioral profiles observed in girls and boys with ASD. This is in part because extant ASD studies have included a small number of girls. Methods: Leveraging the National Database for Autism Research (NDAR), we first analyzed symptom severity in a large sample consisting of 128 ASD girls and 614 age- and IQ-matched ASD boys. We then examined symptom severity and structural imaging data using novel multivariate pattern analysis in a well-matched group of 25 ASD girls, 25 ASD boys, 19 typically developing (TD) girls, and 19 TD boys, obtained from the Autism Brain Imaging Data Exchange (ABIDE). Results: In both the NDAR and ABIDE datasets, girls, compared to boys, with ASD showed less severe repetitive/restricted behaviors (RRBs) and comparable deficits in the social and communication domains. In the ABIDE imaging dataset, gray matter (GM) patterns in the motor cortex, supplementary motor area (SMA), cerebellum, fusiform gyrus, and amygdala accurately discriminated girls and boys with ASD. This sex difference pattern was specific to ASD as the GM in these brain regions did not discriminate TD girls and boys. Moreover, GM in the motor cortex, SMA, and crus 1 subdivision of the cerebellum was correlated with RRB in girls whereas GM in the right putamen-the region that discriminated TD girls and boys-was correlated with RRB in boys. Conclusions: We found robust evidence for reduced levels of RRB in girls, compared to boys, with ASD, providing the strongest evidence to date for sex differences in a core phenotypic feature of childhood ASD. Sex differences in brain morphometry are prominent in the motor system and in areas that comprise the "social brain." Notably, RRB severity is associated with sex differences in GM morphometry in distinct motor regions. Our findings provide novel insights into the neurobiology of sex differences in childhood autism.
    Preview · Article · Sep 2015 · Molecular Autism
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    ABSTRACT: The medial temporal lobe (MTL), encompassing the hippocampus and parahippocampal gyrus (PHG), is a heterogeneous structure which plays a critical role in memory and cognition. Here, we investigate functional architecture of the human MTL along the long axis of the hippocampus and PHG. The hippocampus showed stronger connectivity with striatum, ventral tegmental area and amygdala-regions important for integrating reward and affective signals, whereas the PHG showed stronger connectivity with unimodal and polymodal association cortices. In the hippocampus, the anterior node showed stronger connectivity with the anterior medial temporal lobe and the posterior node showed stronger connectivity with widely distributed cortical and subcortical regions including those involved in sensory and reward processing. In the PHG, differences were characterized by a gradient of increasing anterior-to-posterior connectivity with core nodes of the default mode network. Left and right MTL connectivity patterns were remarkably similar, except for stronger left than right MTL connectivity with regions in the left MTL, the ventral striatum and default mode network. Graph theoretical analysis of MTL-based networks revealed higher node centrality of the posterior, compared to anterior and middle hippocampus. The PHG showed prominent gradients in both node degree and centrality along its anterior-to-posterior axis. Our findings highlight several novel aspects of functional heterogeneity in connectivity along the long axis of the human MTL and provide new insights into how its network organization supports integration and segregation of signals from distributed brain areas. The implications of our findings for a principledunderstanding of distributed pathways that support memory and cognition are discussed.
    Full-text · Article · Sep 2015 · Brain Structure and Function
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    Full-text · Dataset · Aug 2015
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    ABSTRACT: Early numerical proficiency lays the foundation for acquiring quantitative skills essential in today’s technological society. Identification of cognitive and brain markers associated with long-term growth of children’s basic numerical computation abilities is therefore of utmost importance. Previous attempts to relate brain structure and function to numerical competency have focused on behavioral measures from a single time point. Thus, little is known about the brain predictors of individual differences in growth trajectories of numerical abilities. Using a longitudinal design, with multimodal imaging and machine-learning algorithms, we investigated whether brain structure and intrinsic connectivity in early childhood are predictive of 6 year outcomes in numerical abilities spanning childhood and adolescence. Gray matter volume at age 8 in distributed brain regions, including the ventrotemporal occipital cortex (VTOC), the posterior parietal cortex, and the prefrontal cortex, predicted longitudinal gains in numerical, but not reading, abilities. Remarkably, intrinsic connectivity analysis revealed that the strength of functional coupling among these regions also predicted gains in numerical abilities, providing novel evidence for a network of brain regions that works in concert to promote numerical skill acquisition. VTOC connectivity with posterior parietal, anterior temporal, and dorsolateral prefrontal cortices emerged as the most extensive network predicting individual gains in numerical abilities. Crucially, behavioral measures of mathematics, IQ, working memory, and reading did not predict children’s gains in numerical abilities. Our study identifies, for the first time, functional circuits in the human brain that scaffold the development of numerical skills, and highlights potential biomarkers for identifying children at risk for learning difficulties.
    Full-text · Article · Aug 2015 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
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    ABSTRACT: How the brain develops representations for abstract cognitive problems is a major unaddressed question in neuroscience. Here we tackle this fundamental question using arithmetic problem solving, a cognitive domain important for the development of mathematical reasoning. We first examined whether adults demonstrate common neural representations for addition and subtraction problems, two complementary arithmetic operations that manipulate the same quantities. We then examined how the common neural representations for the two problem types change with development. Whole-brain multivoxel representational similarity (MRS) analysis was conducted to examine common coding of addition and subtraction problems in children and adults. We found that adults exhibited significant levels of MRS between the two problem types, not only in the intra-parietal sulcus (IPS) region of the posterior parietal cortex (PPC), but also in ventral temporal-occipital, anterior temporal and dorsolateral prefrontal cortices. Relative to adults, children showed significantly reduced levels of MRS in these same regions. In contrast, no brain areas showed significantly greater MRS between problem types in children. Our findings provide novel evidence that the emergence of arithmetic problem solving skills from childhood to adulthood is characterized by maturation of common neural representations between distinct numerical operations, and involve distributed brain regions important for representing and manipulating numerical quantity. More broadly, our findings demonstrate that representational analysis provides a powerful approach for uncovering fundamental mechanisms by which children develop proficiencies that are a hallmark of human cognition. Copyright © 2015. Published by Elsevier Ltd.
    Full-text · Article · Jul 2015 · Neuropsychologia
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    ABSTRACT: Male predominance is a prominent feature of autism spectrum disorders (ASD), with a reported male to female ratio of 4:1. Because of the overwhelming focus on males, little is known about the neuroanatomical basis of sex differences in ASD. Investigations of sex differences with adequate sample sizes are critical for improving our understanding of the biological mechanisms underlying ASD in females. We leveraged the open-access autism brain imaging data exchange (ABIDE) dataset to obtain structural brain imaging data from 53 females with ASD, who were matched with equivalent samples of males with ASD, and their typically developing (TD) male and female peers. Brain images were processed with FreeSurfer to assess three key features of local cortical morphometry: volume, thickness, and gyrification. A whole-brain approach was used to identify significant effects of sex, diagnosis, and sex-by-diagnosis interaction, using a stringent threshold of p < 0.01 to control for false positives. Stability and power analyses were conducted to guide future research on sex differences in ASD. We detected a main effect of sex in the bilateral superior temporal cortex, driven by greater cortical volume in females compared to males in both the ASD and TD groups. Sex-by-diagnosis interaction was detected in the gyrification of the ventromedial/orbitofrontal prefrontal cortex (vmPFC/OFC). Post-hoc analyses revealed that sex-by-diagnosis interaction was driven by reduced vmPFC/OFC gyrification in males with ASD, compared to females with ASD as well as TD males and females. Finally, stability analyses demonstrated a dramatic drop in the likelihood of observing significant clusters as the sample size decreased, suggesting that previous studies have been largely underpowered. For instance, with a sample of 30 females with ASD (total n = 120), a significant sex-by-diagnosis interaction was only detected in 50 % of the simulated subsamples. Our results demonstrate that some features of typical sex differences are preserved in the brain of individuals with ASD, while others are not. Sex differences in ASD are associated with cortical regions involved in language and social function, two domains of deficits in the disorder. Stability analyses provide novel quantitative insights into why smaller samples may have previously failed to detect sex differences.
    Preview · Article · Jul 2015 · Molecular Autism
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    ABSTRACT: Cognitive control plays an important role in goal-directed behavior, but dynamic brain mechanisms underlying it are poorly understood. Here, using multisite fMRI data from over 100 participants, we investigate causal interactions in three cognitive control tasks within a core Frontal-Cingulate-Parietal network. We found significant causal influences from anterior insula (AI) to dorsal anterior cingulate cortex (dACC) in all three tasks. The AI exhibited greater net causal outflow than any other node in the network. Importantly, a similar pattern of causal interactions was uncovered by two different computational methods for causal analysis. Furthermore, the strength of causal interaction from AI to dACC was greater on high, compared with low, cognitive control trials and was significantly correlated with individual differences in cognitive control abilities. These results emphasize the importance of the AI in cognitive control and highlight its role as a causal hub in the Frontal-Cingulate-Parietal network. Our results further suggest that causal signaling between the AI and dACC plays a fundamental role in implementing cognitive control and are consistent with a two-stage cognitive control model in which the AI first detects events requiring greater access to cognitive control resources and then signals the dACC to execute load-specific cognitive control processes. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
    Preview · Article · Mar 2015 · Cerebral Cortex
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    ABSTRACT: Plasticity of white matter tracts is thought to be essential for cognitive development and academic skill acquisition in children. However, a dearth of high-quality diffusion tensor imaging (DTI) data measuring longitudinal changes with learning, as well as methodological difficulties in multi-time point tract identification have limited our ability to investigate plasticity of specific white matter tracts. Here, we examine learning-related changes of white matter tracts innervating inferior parietal, prefrontal and temporal regions following an intense 2-month math tutoring program. DTI data were acquired from 18 third grade children, both before and after tutoring. A novel fiber tracking algorithm based on a White Matter Query Language (WMQL) was used to identify three sections of the superior longitudinal fasciculus (SLF) linking frontal and parietal (SLF-FP), parietal and temporal (SLF-PT) and frontal and temporal (SLF-FT) cortices, from which we created child-specific probabilistic maps. The SLF-FP, SLF-FT, and SLF-PT tracts identified with the WMQL method were highly reliable across the two time points and showed close correspondence to tracts previously described in adults. Notably, individual differences in behavioral gains after 2 months of tutoring were specifically correlated with plasticity in the left SLF-FT tract. Our results extend previous findings of individual differences in white matter integrity, and provide important new insights into white matter plasticity related to math learning in childhood. More generally, our quantitative approach will be useful for future studies examining longitudinal changes in white matter integrity associated with cognitive skill development.
    Preview · Article · Jan 2015 · Brain Structure and Function
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    ABSTRACT: Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data. To address these issues, we develop a novel consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index. A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum. Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters. A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data. Copyright © 2014. Published by Elsevier B.V.
    No preview · Article · Nov 2014 · Journal of Neuroscience Methods
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    ABSTRACT: The right inferior frontal cortex (rIFC) and the right anterior insula (rAI) have been implicated consistently in inhibitory control, but their differential roles are poorly understood. Here we use multiple quantitative techniques to dissociate the functional organization and roles of the rAI and rIFC. We first conducted a meta-analysis of 70 published inhibitory control studies to generate a commonly activated right fronto-opercular cortex volume of interest (VOI). We then segmented this VOI using two types of features: (1) intrinsic brain activity; and (2) stop-signal task-evoked hemodynamic response profiles. In both cases, segmentation algorithms identified two stable and distinct clusters encompassing the rAI and rIFC. The rAI and rIFC clusters exhibited several distinct functional characteristics. First, the rAI showed stronger intrinsic and task-evoked functional connectivity with the anterior cingulate cortex, whereas the rIFC had stronger intrinsic and task-evoked functional connectivity with dorsomedial prefrontal and lateral fronto-parietal cortices. Second, the rAI showed greater activation than the rIFC during Unsuccessful, but not Successful, Stop trials, and multivoxel response profiles in the rAI, but not the rIFC, accurately differentiated between Successful and Unsuccessful Stop trials. Third, activation in the rIFC, but not rAI, predicted individual differences in inhibitory control abilities. Crucially, these findings were replicated in two independent cohorts of human participants. Together, our findings provide novel quantitative evidence for the dissociable roles of the rAI and rIFC in inhibitory control. We suggest that the rAI is particularly important for detecting behaviorally salient events, whereas the rIFC is more involved in implementing inhibitory control.
    Preview · Article · Oct 2014 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
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    ABSTRACT: Coordinated attention to information from multiple senses is fundamental to our ability to respond to salient environmental events, yet little is known about brain network mechanisms that guide integration of information from multiple senses. Here we investigate dynamic causal mechanisms underlying multisensory auditory–visual attention, focusing on a network of right-hemisphere frontal–cingulate–parietal regions implicated in a wide range of tasks involving attention and cognitive control. Participants performed three ‘oddball’ attention tasks involving auditory, visual and multisensory auditory–visual stimuli during fMRI scanning. We found that the right anterior insula (rAI) demonstrated the most significant causal influences on all other frontal–cingulate–parietal regions, serving as a major causal control hub during multisensory attention. Crucially, we then tested two competing models of the role of the rAI in multisensory attention: an ‘integrated’ signaling model in which the rAI generates a common multisensory control signal associated with simultaneous attention to auditory and visual oddball stimuli versus a ‘segregated’ signaling model in which the rAI generates two segregated and independent signals in each sensory modality. We found strong support for the integrated, rather than the segregated, signaling model. Furthermore, the strength of the integrated control signal from the rAI was most pronounced on the dorsal anterior cingulate and posterior parietal cortices, two key nodes of saliency and central executive networks respectively. These results were preserved with the addition of a superior temporal sulcus region involved in multisensory processing. Our study provides new insights into the dynamic causal mechanisms by which the AI facilitates multisensory attention.
    Full-text · Article · Oct 2014 · European Journal of Neuroscience

  • No preview · Conference Paper · Oct 2014
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    Lucina Q. Uddin · Vinod Menon

    Full-text · Dataset · Aug 2014

Publication Stats

24k Citations
1,313.02 Total Impact Points

Institutions

  • 1999-2015
    • Stanford Medicine
      • • Department of Psychiatry and Behavioral Sciences
      • • Department of Neurology and Neurological Sciences
      Stanford, California, United States
  • 1997-2015
    • Stanford University
      • • Department of Psychiatry and Behavioral Sciences
      • • Department of Medicine
      Stanford, California, United States
  • 2011
    • University of Pittsburgh
      Pittsburgh, Pennsylvania, United States
  • 2003
    • University of Geneva
      • Division of Child Psychiatry
      Genève, Geneva, Switzerland
  • 1996
    • VA Palo Alto Health Care System
      Palo Alto, California, United States