Janaina Mourao-Miranda

Summary

Affiliation: University College London
Country: UK

Publications

  1. pmc Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression
    Janaina Mourao-Miranda
    Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK
    Bipolar Disord 14:451-60. 2012
  2. pmc Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents
    Janaina Mourao-Miranda
    Department of Computer Science, University College London, United Kingdom
    PLoS ONE 7:e29482. 2012
  3. pmc Patient classification as an outlier detection problem: an application of the One-Class Support Vector Machine
    Janaina Mourao-Miranda
    Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King s College London, London, UK
    Neuroimage 58:793-804. 2011
  4. doi request reprint Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach
    Christine Ecker
    Section of Brain Maturation, Department of Psychological Medicine, Institute of Psychiatry, Brain Image Analysis Unit, Centre for Neuroimaging Sciences, King s College, London SE5 8AF, United Kingdom
    J Neurosci 30:10612-23. 2010
  5. ncbi request reprint Dynamic discrimination analysis: a spatial-temporal SVM
    Janaina Mourao-Miranda
    Brain Image Analysis Unit, Biostatics Department, Centre for Neuroimaging Sciences PO 89, Institute of Psychiatry, KCL, De Crespigny Park, London SE5 8AF, UK
    Neuroimage 36:88-99. 2007
  6. ncbi request reprint Pattern classification of sad facial processing: toward the development of neurobiological markers in depression
    Cynthia H Y Fu
    Institute of Psychiatry, King s College London, De Crespigny Park, London, United Kingdom
    Biol Psychiatry 63:656-62. 2008
  7. ncbi request reprint Unsupervised analysis of fMRI data using kernel canonical correlation
    David R Hardoon
    The Centre for Computational Statistics and Machine Learning, Department of Computer Science, University College London, UK
    Neuroimage 37:1250-9. 2007
  8. doi request reprint Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach
    Christine Ecker
    Section of Brain Maturation, Department of Psychological Medicine, Institute of Psychiatry, King s College, London, UK
    Neuroimage 49:44-56. 2010
  9. pmc What does brain response to neutral faces tell us about major depression? evidence from machine learning and fMRI
    Leticia Oliveira
    Department of Neuroimaging, King s College London, London, United Kingdom
    PLoS ONE 8:e60121. 2013
  10. ncbi request reprint The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data
    Janaina Mourao-Miranda
    Biostatics Department, Centre for Neuroimaging Sciences, Institute of Psychiatry, KCL, London, UK
    Neuroimage 33:1055-65. 2006

Detail Information

Publications16

  1. pmc Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression
    Janaina Mourao-Miranda
    Department of Computer Science, Centre for Computational Statistics and Machine Learning, University College London, London, UK
    Bipolar Disord 14:451-60. 2012
    ..Only a few studies have applied similar methods to functional MRI (fMRI) data...
  2. pmc Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents
    Janaina Mourao-Miranda
    Department of Computer Science, University College London, United Kingdom
    PLoS ONE 7:e29482. 2012
    ..identify those healthy genetically at-risk adolescents who were most likely to develop future Axis I disorders...
  3. pmc Patient classification as an outlier detection problem: an application of the One-Class Support Vector Machine
    Janaina Mourao-Miranda
    Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King s College London, London, UK
    Neuroimage 58:793-804. 2011
    ..However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers...
  4. doi request reprint Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach
    Christine Ecker
    Section of Brain Maturation, Department of Psychological Medicine, Institute of Psychiatry, Brain Image Analysis Unit, Centre for Neuroimaging Sciences, King s College, London SE5 8AF, United Kingdom
    J Neurosci 30:10612-23. 2010
    ..The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition...
  5. ncbi request reprint Dynamic discrimination analysis: a spatial-temporal SVM
    Janaina Mourao-Miranda
    Brain Image Analysis Unit, Biostatics Department, Centre for Neuroimaging Sciences PO 89, Institute of Psychiatry, KCL, De Crespigny Park, London SE5 8AF, UK
    Neuroimage 36:88-99. 2007
    ..This produces a discriminating weight vector encompassing both voxels and time. The resulting vector furnishes discriminating responses, at each voxel without imposing any constraints on their temporal form...
  6. ncbi request reprint Pattern classification of sad facial processing: toward the development of neurobiological markers in depression
    Cynthia H Y Fu
    Institute of Psychiatry, King s College London, De Crespigny Park, London, United Kingdom
    Biol Psychiatry 63:656-62. 2008
    ..We sought to examine the sensitivity and specificity of whole brain pattern classification of implicit processing of sad facial expressions in depression...
  7. ncbi request reprint Unsupervised analysis of fMRI data using kernel canonical correlation
    David R Hardoon
    The Centre for Computational Statistics and Machine Learning, Department of Computer Science, University College London, UK
    Neuroimage 37:1250-9. 2007
    ..The results of the KCCA were achieved blind to the categorical task labels. Instead, the stimulus category is effectively derived from the vector of image features...
  8. doi request reprint Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach
    Christine Ecker
    Section of Brain Maturation, Department of Psychological Medicine, Institute of Psychiatry, King s College, London, UK
    Neuroimage 49:44-56. 2010
    ..Also, these differences provide significant predictive power for group membership, which is related to symptom severity...
  9. pmc What does brain response to neutral faces tell us about major depression? evidence from machine learning and fMRI
    Leticia Oliveira
    Department of Neuroimaging, King s College London, London, United Kingdom
    PLoS ONE 8:e60121. 2013
    ..The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients...
  10. ncbi request reprint The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data
    Janaina Mourao-Miranda
    Biostatics Department, Centre for Neuroimaging Sciences, Institute of Psychiatry, KCL, London, UK
    Neuroimage 33:1055-65. 2006
    ..However, in a multi-subject level, the temporal compression improved the performance of the SVM, but the space selection had no effect on the classification accuracy...
  11. doi request reprint Neuroanatomy of verbal working memory as a diagnostic biomarker for depression
    Andre F Marquand
    Institute of Psychiatry, King s College London, De Crespigny Park, London, UK
    Neuroreport 19:1507-11. 2008
    ....
  12. doi request reprint Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes
    Andre Marquand
    Department of Clinical Neuroscience, Centre for Neuroimaging Sciences, Institute of Psychiatry, King s College London, UK
    Neuroimage 49:2178-89. 2010
    ..For classification, GP models perform categorical prediction as accurately as a support vector machine classifier and furnish probabilistic class predictions...
  13. pmc Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine, and placebo in healthy volunteers
    Andre F Marquand
    Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King s College London, London, UK
    Neuropsychopharmacology 36:1237-47. 2011
    ..Thus, interactions between drug effects and motivational state are crucial in defining the effects of MPH and ATX...
  14. pmc Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach
    Andre F Marquand
    Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King s College London, London, United Kingdom
    PLoS ONE 8:e69237. 2013
    ....
  15. doi request reprint Correlation-based multivariate analysis of genetic influence on brain volume
    David R Hardoon
    Computational Statistics and Machine Learning Centre, Dept of Computer Science, University College London, London WC1E 6BT, United Kingdom
    Neurosci Lett 450:281-6. 2009
    ..The implementation of the method is demonstrated on genetic and structural magnetic resonance imaging (MRI) data acquired from a group of 16 healthy subjects by showing the multivariate genetic influence on grey and white matter...
  16. doi request reprint Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression
    Sergi G Costafreda
    Institute of Psychiatry, King s College London, De Crespigny Park, London, UK
    Neuroreport 20:637-41. 2009
    ..The functional neuroanatomy of sad faces at the lowest and highest intensities identified patients, before the initiation of therapy, who had a full clinical response to CBT (sensitivity 71%, specificity 86%, P = 0.029)...