Vinod Menon

Stanford Medicine, Stanford, California, United States

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Publications (184)1207.26 Total impact

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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.
    Cerebral Cortex 03/2015; DOI:10.1093/cercor/bhv046 · 8.31 Impact Factor
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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.
    Brain Structure and Function 01/2015; DOI:10.1007/s00429-014-0975-6 · 4.57 Impact Factor
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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.
    Journal of Neuroscience Methods 11/2014; 240. DOI:10.1016/j.jneumeth.2014.11.014 · 1.96 Impact Factor
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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.
    The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 10/2014; 34(44):14652-67. DOI:10.1523/JNEUROSCI.3048-14.2014 · 6.75 Impact Factor
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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.
    European Journal of Neuroscience 10/2014; 41(2). DOI:10.1111/ejn.12764 · 3.67 Impact Factor
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    Lucina Q. Uddin, Vinod Menon
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    ABSTRACT: The importance of the hippocampal system for rapid learning and memory is well recognized, but its contributions to a cardinal feature of children's cognitive development-the transition from procedure-based to memory-based problem-solving strategies-are unknown. Here we show that the hippocampal system is pivotal to this strategic transition. Longitudinal functional magnetic resonance imaging (fMRI) in 7-9-year-old children revealed that the transition from use of counting to memory-based retrieval parallels increased hippocampal and decreased prefrontal-parietal engagement during arithmetic problem solving. Longitudinal improvements in retrieval-strategy use were predicted by increased hippocampal-neocortical functional connectivity. Beyond childhood, retrieval-strategy use continued to improve through adolescence into adulthood and was associated with decreased activation but more stable interproblem representations in the hippocampus. Our findings provide insights into the dynamic role of the hippocampus in the maturation of memory-based problem solving and establish a critical link between hippocampal-neocortical reorganization and children's cognitive development.
    Nature Neuroscience 08/2014; 17(9). DOI:10.1038/nn.3788 · 14.98 Impact Factor
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    ABSTRACT: Developmental dyscalculia (DD) is marked by specific deficits in processing numerical and mathematical information despite normal intelligence (IQ) and reading ability. We examined how brain circuits used by young children with DD to solve simple addition and subtraction problems differ from those used by typically developing (TD) children who were matched on age, IQ, reading ability, and working memory. Children with DD were slower and less accurate during problem solving than TD children, and were especially impaired on their ability to solve subtraction problems. Children with DD showed significantly greater activity in multiple parietal, occipito-temporal and prefrontal cortex regions while solving addition and subtraction problems. Despite poorer performance during subtraction, children with DD showed greater activity in multiple intra-parietal sulcus (IPS) and superior parietal lobule subdivisions in the dorsal posterior parietal cortex as well as fusiform gyrus in the ventral occipito-temporal cortex. Critically, effective connectivity analyses revealed hyper-connectivity, rather than reduced connectivity, between the IPS and multiple brain systems including the lateral fronto-parietal and default mode networks in children with DD during both addition and subtraction. These findings suggest the IPS and its functional circuits are a major locus of dysfunction during both addition and subtraction problem solving in DD, and that inappropriate task modulation and hyper-connectivity, rather than under-engagement and under-connectivity, are the neural mechanisms underlying problem solving difficulties in children with DD. We discuss our findings in the broader context of multiple levels of analysis and performance issues inherent in neuroimaging studies of typical and atypical development.
    Developmental Science 08/2014; 18(3). DOI:10.1111/desc.12216 · 3.89 Impact Factor
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    ABSTRACT: Background / Purpose: Children with autism spectrum disorders (ASD) often demonstrate remarkable cognitive strengths in domains such as mathematics. These, however, tend to be overlooked as emphasis is placed on impairments such as social communication deficits. Diffusion tensor imaging (DTI) provides a means to probe the subtle structural brain differences found between children with ASD and typically developing (TD) children. Recent studies have described the structure of a white-matter pathway connecting the hippocampus and fusiform gyrus, and have suggested the involvement of the fusiform gyrus in mathematical abilities in ASD. Main conclusion: Results suggest that a single white-matter tract, namely the fusiform-hippocampal pathway, is related to both strengths and deficits in children with ASD. A source of impairment, specifically social communication deficits, can also give rise to remarkable strengths in other domains such as mathematics.
    69th Society of Biological Psychiatry Annual Meeting 2014; 06/2014
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    ABSTRACT: Background: Autism spectrum disorders (ASDs) are often accompanied by an uneven profile of cognitive capacities. From rare reports of savant skills to the consistent finding of stronger performance than verbal IQ, spared and enhanced abilities in autism tend to involve veridical memory and visual-spatial processing. While little is known regarding the neural underpinnings of enhanced abilities in children with autism, these areas of strength point towards the involvement of medial temporal and parietal lobe structures. Resting state functional MRI provides a powerful method for probing intrinsic connectivity between brain regions. Recent studies using the method have revealed a conflicting picture of both hypo- and hyper-connectivity in individuals with ASD relative to typically developing (TD) peers. Objectives: We sought to understand whether patterns of intrinsic functional brain connectivity in children with ASD could be related to cognitive strengths. We focused on connectivity of the hippocampus, an area critically involved in memory and spatial navigation. Methods: Six minutes of resting state functional MRI was collected in 20 children (aged 7-12) with ASD and 19 TD children, matched on age, IQ, and gender. We compared functional connectivity between groups from left and right anterior hippocampal seeds, an area implicated in binding multiple visual-spatial features. Parent report of exceptional abilities from the ADI-R were used to compare connectivity measures amongst children with ASD. Support vector regression (SVR) was used to probe whether areas differing in hippocampal connectivity could predict verbal IQ (VIQ) and performance IQ (PIQ) in TD children and children with ASD. Results: Children with ASD displayed patterns of both hypo- and hyper-connectivity of the hippocampus with other cortical and subcortical regions. TD children had greater connectivity of the hippocampus to the posterior cingulate, ventral medial prefrontal cortex and parahippocampal gyrus, all areas implicated in the default mode network and self-related processing. In contrast, children with ASD had greater connectivity from right hippocampus to the right intraparietal sulcus and the adjoining angular gyrus, areas implicated in visual-spatial processing. Hippocampal-parietal connectivity was strongest in children with ASD whose parents reported they had exceptional visual-spatial abilities. SVR analyses revealed that the pattern of hippocampal connectivity in this parietal cluster could predict PIQ scores for children with ASD but not for TD children. Hippocampal-parietal connectivity did not predict VIQ for either group. Conclusions: These results suggest that hippocampal-parietal hyper-connectivity in children with ASD is related to spared and enhanced non-verbal intelligence and visual spatial abilities in these children.
    2014 International Meeting for Autism Research; 05/2014
  • M. Schaer, V. Menon
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    ABSTRACT: Background: Numerous published studies have delineated morphometric differences in the brain of children, adolescents and adults with autism spectrum disorders (ASD). However, most of these studies analyzed limited sample size, or specific populations in terms of age range, symptoms’ severity or cognitive abilities, so that large-scale studies are needed to better understand how demographics and clinical profile influence the cerebral phenotype in ASD. Objectives: This study aims at exploiting the largest sample of subjects available to date to examine the structural brain differences in patients with ASD as compared to controls. Further, we sought to explore potential structural correlates of the varying symptoms’ severity and cognitive level in autism. Methods: The ABIDE data set (http://fcon_1000.projects.nitrc.org/indi/abide/) comprises 1112 structural MRI, collected from 539 patients with ASD and 573 controls aged between 6.5 and 64 years old. Volumetric estimations and 3D cortical reconstructions were obtained using FreeSurfer (http://surfer.nmr.mgh.harvard.edu). As the ABIDE dataset is distributed without any quality control, intensive inspection was achieved for each subject and manual edits were used as needed. Quality control was conducted in 852 MRIs from 13 sites to date, among which 128 MRIs (15%) were excluded because of motion, artifact or poor cortical reconstruction. The resulting 724 scans were used to compare cerebral and regional cortical and white matter volumes between controls and ASD. Further analyses were conducted within the ASD group to correlate cerebral morphometry with symptoms’ severity as measured with the ADOS (Autism Diagnostic Observation Scale, Lord et al.) and with IQ. Results: In the entire sample, no difference in global brain volume was observed in ASD as compared to controls. Trends for increased cortical volume in ASD was observed in the bilateral superior temporal gyri, right precuneus and left isthmus of the cingulate (p<0.05, uncorrected for multiple comparisons). Within the ASD group, patients that had higher severity of symptoms had larger cerebral volumes (cortical, white and subcortical) than patients with lower severity of symptoms (all p<0.002). At the regional level, this increased volume in the most severely affected patients was mostly lateralized in the left hemisphere, affecting prefrontal medial and lateral regions, inferior and medial temporal areas, as well as the parieto-temporo-occipital junction. We also observed that the patients with ASD with the lower IQ had smaller cerebral and white matter volume as compared with those with higher IQ. Conclusions: In this large sample of patients with ASD, we observed that ASD diagnosis alone was not a significant parameter related to different brain morphometry, suggesting that the clinical heterogeneity is also related to heterogeneous cerebral phenotype. Disentangling the different direction of the effect of higher symptom severity and lower cognitive abilities may help reconciliate previously divergent results and provide a framework to better understand the spectrum of neurodevelopmental pathways that can lead to autism.
    2014 International Meeting for Autism Research; 05/2014
  • The Lancet 02/2014; 383:S65. DOI:10.1016/S0140-6736(14)60328-7 · 45.22 Impact Factor
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    ABSTRACT: Autism spectrum disorders (ASD) are characterized by social impairments alongside cognitive and behavioral inflexibility. While social deficits in ASD have been extensively characterized, the neurobiological basis of inflexibility and its relation to core clinical symptoms of the disorder are unknown. We acquired functional neuroimaging data from two cohorts, each consisting of 17 children with ASD and 17 age- and IQ-matched typically developing (TD) children, during stimulus-evoked brain states involving performance of social attention and numerical problem solving tasks, as well as during intrinsic, resting brain states. Effective connectivity between key nodes of the salience network, default mode network, and central executive network was used to obtain indices of functional organization across evoked and intrinsic brain states. In both cohorts examined, a machine learning algorithm was able to discriminate intrinsic (resting) and evoked (task) functional brain network configurations more accurately in TD children than in children with ASD. Brain state discriminability was related to severity of restricted and repetitive behaviors, indicating that weak modulation of brain states may contribute to behavioral inflexibility in ASD. These findings provide novel evidence for a potential link between neurophysiological inflexibility and core symptoms of this complex neurodevelopmental disorder.
    Cerebral Cortex 01/2014; DOI:10.1093/cercor/bhu161 · 8.31 Impact Factor
  • International Conference on Music Perception and Cognition, Seoul; 01/2014
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    ABSTRACT: Autism spectrum disorder (ASD), a neurodevelopmental disorder affecting nearly 1 in 88 children, is thought to result from aberrant brain connectivity. Remarkably, there have been no systematic attempts to characterize whole-brain connectivity in children with ASD. Here, we use neuroimaging to show that there are more instances of greater functional connectivity in the brains of children with ASD in comparison to those of typically developing children. Hyperconnectivity in ASD was observed at the whole-brain and subsystems levels, across long- and short-range connections, and was associated with higher levels of fluctuations in regional brain signals. Brain hyperconnectivity predicted symptom severity in ASD, such that children with greater functional connectivity exhibited more severe social deficits. We replicated these findings in two additional independent cohorts, demonstrating again that at earlier ages, the brain of children with ASD is largely functionally hyperconnected in ways that contribute to social dysfunction. Our findings provide unique insights into brain mechanisms underlying childhood autism.
    Cell Reports 11/2013; DOI:10.1016/j.celrep.2013.10.001 · 7.21 Impact Factor
  • Vinod Menon
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    ABSTRACT: The human brain undergoes protracted developmental changes during which it constructs functional networks that engender complex cognitive abilities. Understanding brain function ultimately depends on knowledge of how dynamic interactions between distributed brain regions mature with age to produce sophisticated cognitive systems. This review summarizes recent progress in our understanding of the ontogeny of functional brain networks. Here I describe how complementary methods for probing functional connectivity are providing unique insights into the emergence and maturation of distinct functional networks from childhood to adulthood. I highlight six emerging principles governing the development of large-scale functional networks and discuss how they inform cognitive and affective function in typically developing children and in children with neurodevelopmental disorders.
    Trends in Cognitive Sciences 10/2013; DOI:10.1016/j.tics.2013.09.015 · 21.15 Impact Factor
  • Proceedings of the National Academy of Sciences 10/2013; 110(42):E3974. DOI:10.1073/pnas.1313455110 · 9.81 Impact Factor
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    ABSTRACT: Baddeley and Hitch's multi-component working memory (WM) model has played an enduring and influential role in our understanding of cognitive abilities. Very little is known, however, about the neural basis of this multi-component WM model and the differential role each component plays in mediating arithmetic problem solving abilities in children. Here, we investigate the neural basis of the central executive (CE), phonological (PL) and visuo-spatial (VS) components of WM during a demanding mental arithmetic task in 7-9 year old children (N=74). The VS component was the strongest predictor of math ability in children and was associated with increased arithmetic complexity-related responses in left dorsolateral and right ventrolateral prefrontal cortices as well as bilateral intra-parietal sulcus and supramarginal gyrus in posterior parietal cortex. Critically, VS, CE and PL abilities were associated with largely distinct patterns of brain response. Overlap between VS and CE components was observed in left supramarginal gyrus and no overlap was observed between VS and PL components. Our findings point to a central role of visuo-spatial WM during arithmetic problem-solving in young grade-school children and highlight the usefulness of the multi-component Baddeley and Hitch WM model in fractionating the neural correlates of arithmetic problem solving during development.
    10/2013; 6C:162-175. DOI:10.1016/j.dcn.2013.10.001
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    ABSTRACT: Early childhood anxiety has been linked to an increased risk for developing mood and anxiety disorders. Little, however, is known about its effect on the brain during a period in early childhood when anxiety-related traits begin to be reliably identifiable. Even less is known about the neurodevelopmental origins of individual differences in childhood anxiety. We combined structural and functional magnetic resonance imaging with neuropsychological assessments of anxiety based on daily life experiences to investigate the effects of anxiety on the brain in 76 young children. We then used machine learning algorithms with balanced cross-validation to examine brain-based predictors of individual differences in childhood anxiety. Even in children as young as ages 7 to 9, high childhood anxiety is associated with enlarged amygdala volume and this enlargement is localized specifically to the basolateral amygdala. High childhood anxiety is also associated with increased connectivity between the amygdala and distributed brain systems involved in attention, emotion perception, and regulation, and these effects are most prominent in basolateral amygdala. Critically, machine learning algorithms revealed that levels of childhood anxiety could be reliably predicted by amygdala morphometry and intrinsic functional connectivity, with the left basolateral amygdala emerging as the strongest predictor. Individual differences in anxiety can be reliably detected with high predictive value in amygdala-centric emotion circuits at a surprisingly young age. Our study provides important new insights into the neurodevelopmental origins of anxiety and has significant implications for the development of predictive biomarkers to identify children at risk for anxiety disorders.
    Biological psychiatry 10/2013; DOI:10.1016/j.biopsych.2013.10.006 · 9.47 Impact Factor

Publication Stats

20k Citations
1,207.26 Total Impact Points

Institutions

  • 1997–2015
    • Stanford Medicine
      • • Department of Psychiatry and Behavioral Sciences
      • • Department of Neurology and Neurological Sciences
      Stanford, California, United States
  • 1998–2014
    • Stanford University
      • • Department of Neurology and Neurological Sciences
      • • Department of Psychiatry and Behavioral Sciences
      • • Division of Child and Adolescent Psychiatry
      Palo Alto, California, United States
  • 2009
    • McGill University
      • Department of Psychology
      Montréal, Quebec, Canada
  • 2004
    • University of Glasgow
      Glasgow, Scotland, United Kingdom
  • 1996
    • VA Palo Alto Health Care System
      Palo Alto, California, United States