Vinod Menon

Stanford University, Palo Alto, California, United States

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Publications (179)1155.89 Total impact

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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; · 7.84 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; · 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.
    Journal of Neuroscience 10/2014; 34(44):14652-67. · 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; · 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; · 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; · 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
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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; · 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; · 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; · 21.15 Impact Factor
  • Proceedings of the National Academy of Sciences 10/2013; 110(42):E3974. · 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.
    Developmental cognitive neuroscience. 10/2013; 6C:162-175.
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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; · 8.93 Impact Factor
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    ABSTRACT: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder.
    Biological psychiatry 08/2013; · 8.93 Impact Factor
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    ABSTRACT: The study of developmental disorders can provide a unique window into the role of domain-general cognitive abilities and neural systems in typical and atypical development. Mathematical disabilities (MD) are characterized by marked difficulty in mathematical cognition in the presence of preserved intelligence and verbal ability. Although studies of MD have most often focused on the role of core deficits in numerical processing, domain-general cognitive abilities, in particular working memory (WM), have also been implicated. Here we identify specific WM components that are impaired in children with MD and then examine their role in arithmetic problem solving. Compared to typically developing (TD) children, the MD group demonstrated lower arithmetic performance and lower visuo-spatial working memory (VSWM) scores with preserved abilities on the phonological and central executive components of WM. Whole brain analysis revealed that, during arithmetic problem solving, left posterior parietal cortex, bilateral dorsolateral and ventrolateral prefrontal cortex, cingulate gyrus and precuneus, and fusiform gyrus responses were positively correlated with VSWM ability in TD children, but not in the MD group. Additional analyses using a priori posterior parietal cortex regions previously implicated in WM tasks, demonstrated a convergent pattern of results during arithmetic problem solving. These results suggest that MD is characterized by a common locus of arithmetic and VSWM deficits at both the cognitive and functional neuroanatomical levels. Unlike TD children, children with MD do not use VSWM resources appropriately during arithmetic problem solving. This work advances our understanding of VSWM as an important domain-general cognitive process in both typical and atypical mathematical skill development.
    Neuropsychologia 07/2013; · 3.45 Impact Factor
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    ABSTRACT: IMPORTANCE Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood. OBJECTIVES To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD. DESIGN, SETTING, AND PARTICIPANTS Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children. MAIN OUTCOMES AND MEASURES Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD. RESULTS We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual's salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen-level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity. CONCLUSIONS AND RELEVANCE Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD.
    JAMA Psychiatry 06/2013; · 12.01 Impact Factor
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    ABSTRACT: Anhedonia is the inability to experience pleasure from normally pleasant stimuli. Although anhedonia is a prominent feature of many psychiatric disorders, trait anhedonia is also observed dimensionally in healthy individuals. Currently, the neurobiological basis of anhedonia is poorly understood because it has been mainly investigated in patients with psychiatric disorders. Thus, previous studies have not been able to adequately disentangle the neural correlates of anhedonia from other clinical symptoms. In this study, trait anhedonia was assessed in well-characterized healthy participants with no history of Axis I psychiatric illness. Functional magnetic resonance imaging with musical stimuli was used to examine brain responses and effective connectivity in relation to individual differences in anhedonia. We found that trait anhedonia was negatively correlated with pleasantness ratings of music stimuli and with activation of key brain structures involved in reward processing, including nucleus accumbens (NAc), basal forebrain and hypothalamus which are linked by the medial forebrain bundle to the ventral tegmental area (VTA). Brain regions important for processing salient emotional stimuli, including anterior insula and orbitofrontal cortex were also negatively correlated with trait anhedonia. Furthermore, effective connectivity between NAc, VTA and paralimbic areas, that regulate emotional reactivity to hedonic stimuli, was negatively correlated with trait anhedonia. Our results indicate that trait anhedonia is associated with reduced reactivity and connectivity of mesolimbic and related limbic and paralimbic systems involved in reward processing. Critically, this association can be detected even in individuals without psychiatric illness. Our findings have important implications both for understanding the neurobiological basis of anhedonia and for the treatment of anhedonia in psychiatric disorders.
    Journal of Psychiatric Research 06/2013; · 4.09 Impact Factor

Publication Stats

17k Citations
1,155.89 Total Impact Points


  • 1997–2014
    • Stanford University
      • • Stanford Institute for Neuro-Innovation and Translational Neurosciences (SINTN)
      • • Department of Psychiatry and Behavioral Sciences
      Palo Alto, California, United States
    • Stanford Medicine
      • • Department of Psychiatry and Behavioral Sciences
      • • Department of Neurology and Neurological Sciences
      Stanford, California, United States
  • 2013
    • University College London
      Londinium, England, United Kingdom
    • Hebrew University of Jerusalem
      • School of Education
      Jerusalem, Jerusalem District, Israel
  • 2003–2012
    • McGill University
      • • School of Computer Science
      • • Department of Psychology
      Montréal, Quebec, Canada
    • University of California, San Diego
      • Department of Psychiatry
      San Diego, CA, United States
  • 2009
    • Pierre and Marie Curie University - Paris 6
      Lutetia Parisorum, Île-de-France, France
  • 2008
    • Medical University of South Carolina
      Charleston, South Carolina, United States
  • 2006
    • Children's Hospital of Orange County
      Orange Cove, California, United States
  • 2002–2005
    • University of California, Davis
      • Department of Psychology
      Davis, CA, United States
  • 2004
    • University of Glasgow
      Glasgow, Scotland, United Kingdom