Kaustubh Supekar

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. pmc Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model
    Kaustubh Supekar
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
    PLoS Comput Biol 8:e1002374. 2012
  2. pmc Development of functional and structural connectivity within the default mode network in young children
    Kaustubh Supekar
    Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94304, USA
    Neuroimage 52:290-301. 2010
  3. pmc Development of large-scale functional brain networks in children
    Kaustubh Supekar
    Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
    PLoS Biol 7:e1000157. 2009
  4. doi request reprint Salience network-based classification and prediction of symptom severity in children with autism
    Lucina Q Uddin
    Departments of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
    JAMA Psychiatry 70:869-79. 2013
  5. pmc Underconnectivity between voice-selective cortex and reward circuitry in children with autism
    Daniel A Abrams
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA 94304, USA
    Proc Natl Acad Sci U S A 110:12060-5. 2013
  6. pmc Dissociable connectivity within human angular gyrus and intraparietal sulcus: evidence from functional and structural connectivity
    Lucina Q Uddin
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
    Cereb Cortex 20:2636-46. 2010
  7. pmc Network analysis of intrinsic functional brain connectivity in Alzheimer's disease
    Kaustubh Supekar
    Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
    PLoS Comput Biol 4:e1000100. 2008
  8. pmc A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 65:83-96. 2013
  9. pmc Immature integration and segregation of emotion-related brain circuitry in young children
    Shaozheng Qin
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
    Proc Natl Acad Sci U S A 109:7941-6. 2012
  10. pmc Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 59:3852-61. 2012

Detail Information

Publications17

  1. pmc Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model
    Kaustubh Supekar
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
    PLoS Comput Biol 8:e1002374. 2012
    ..The quantitative approach developed is likely to be useful in investigating neurodevelopmental disorders, in which control processes are impaired, such as autism and ADHD...
  2. pmc Development of functional and structural connectivity within the default mode network in young children
    Kaustubh Supekar
    Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94304, USA
    Neuroimage 52:290-301. 2010
    ..More generally, our study demonstrates how quantitative multimodal analysis of anatomy and connectivity allows us to better characterize the heterogeneous development and maturation of brain networks...
  3. pmc Development of large-scale functional brain networks in children
    Kaustubh Supekar
    Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
    PLoS Biol 7:e1000157. 2009
    ....
  4. doi request reprint Salience network-based classification and prediction of symptom severity in children with autism
    Lucina Q Uddin
    Departments of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
    JAMA Psychiatry 70:869-79. 2013
    ..Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD. ..
  5. pmc Underconnectivity between voice-selective cortex and reward circuitry in children with autism
    Daniel A Abrams
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA 94304, USA
    Proc Natl Acad Sci U S A 110:12060-5. 2013
    ..Our study provides support for the social motivation theory of ASD. ..
  6. pmc Dissociable connectivity within human angular gyrus and intraparietal sulcus: evidence from functional and structural connectivity
    Lucina Q Uddin
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
    Cereb Cortex 20:2636-46. 2010
    ....
  7. pmc Network analysis of intrinsic functional brain connectivity in Alzheimer's disease
    Kaustubh Supekar
    Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
    PLoS Comput Biol 4:e1000100. 2008
    ..Small-world metrics can characterize the functional organization of the brain in AD, and our findings further suggest that these network measures may be useful as an imaging-based biomarker to distinguish AD from healthy aging...
  8. pmc A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 65:83-96. 2013
    ..Taken together, our findings suggest that our method is a powerful tool for investigating functional subdivisions in the human brain...
  9. pmc Immature integration and segregation of emotion-related brain circuitry in young children
    Shaozheng Qin
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
    Proc Natl Acad Sci U S A 109:7941-6. 2012
    ..These immature patterns of amygdala connectivity have important implications for understanding typical and atypical development of emotion-related brain circuitry...
  10. pmc Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 59:3852-61. 2012
    ..Taken together, our findings suggest that SPC-EN provides a powerful tool for characterizing connectivity involving a large number of correlated regions that span the entire brain...
  11. pmc Resting-state functional connectivity reflects structural connectivity in the default mode network
    Michael D Greicius
    Department of Neurology, Stanford University School of Medicine, Stanford, CA 94304, USA
    Cereb Cortex 19:72-8. 2009
    ..The results demonstrate that resting-state functional connectivity reflects structural connectivity and that combining modalities can enrich our understanding of these canonical brain networks...
  12. ncbi request reprint Knowledge Zone: a public repository of peer-reviewed biomedical ontologies
    Kaustubh Supekar
    Stanford Medical Informatics, Stanford University School of Medicine, USA
    Stud Health Technol Inform 129:812-6. 2007
    ....
  13. pmc Multivariate dynamical systems models for estimating causal interactions in fMRI
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305 5778, USA
    Neuroimage 54:807-23. 2011
    ..Our study suggests that VB estimation of MDS provides a robust method for estimating and interpreting causal network interactions in fMRI data...
  14. pmc Neural predictors of individual differences in response to math tutoring in primary-grade school children
    Kaustubh Supekar
    Departments of Psychiatry and Behavioral Sciences and Neurology and Neurological Sciences, Program in Neuroscience, and Symbolic Systems Program, Stanford University School of Medicine, Stanford, CA 94304
    Proc Natl Acad Sci U S A 110:8230-5. 2013
    ..More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures...
  15. pmc Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits
    Charles J Lynch
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
    Biol Psychiatry 74:212-9. 2013
    ..Furthermore, the functionally heterogeneous profile of the posteromedial cortex raises questions regarding how altered connectivity manifests in specific functional modules within this brain region in children with ASD...
  16. pmc Typical and atypical development of functional human brain networks: insights from resting-state FMRI
    Lucina Q Uddin
    Stanford Cognitive and Systems Neuroscience Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine Stanford, CA, USA
    Front Syst Neurosci 4:21. 2010
    ..We conclude by identifying critical gaps in the current literature, discussing methodological issues, and suggesting avenues for future research...
  17. pmc Sparse logistic regression for whole-brain classification of fMRI data
    Srikanth Ryali
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
    Neuroimage 51:752-64. 2010
    ..These findings suggest that our method is not only computationally efficient, but it also achieves the twin objectives of identifying relevant discriminative brain regions and accurately classifying fMRI data...