Bio

Bio


I currently hold a joint postdoctoral position at the Functional Imaging in Neuropsychiatric Disorders (FIND) lab of Stanford University and the Parietal team of INRIA under the mentorship of Dr. Michael Greicius and Dr. Bertrand Thirion. I was previously a member of the Biomedical Signal and Image Computing Lab (BiSICL) and earned my Ph.D degree in electrical engineering at the University of British Columbia (UBC) under the guidance of Dr. Rafeef Abugharbieh. I obtained my M.A.Sc degree in electrical engineering at UBC under the joint supervision of Dr. Rafeef Abugharbieh and Dr. Martin McKeown and received my B.A.Sc. degree in electronics engineering at Simon Fraser University (SFU) working under Dr. Theodore Milner and Dr. David Franklin.

Professional Education


  • Doctor of Philosophy, The University of British Columbia, Electrical and Computer Engineering (2011)
  • Master of Applied Science, The University of British Columbia, Electrical and Computer Engineering (2007)
  • Bachelor of Applied Science, Simon Fraser University, Electronics Engineering (2005)

Stanford Advisors


Research & Scholarship

Current Research and Scholarly Interests


My research interest lies in developing new machine learning and statistical methods for biomedical applications. My current research focus is on the development of new multivariate methods for high dimensional prediction to investigate the relationships between genetics, brain, and behaviour. I am also devising new graphical models for multimodal integration of neuroimaging data and new approaches based on Riemannian statistics for covariance-based classification. To facilitate result interpretation, I will be pursuing research in high dimensional inference and developing new statistical techniques for addressing the open problem of significant feature identification in high dimensional models.

Publications

Journal Articles


  • Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm Cerebral Cortex Milazzo, A. C., Ng, B., Jiang, H., Shirer, W., Varoquaux, G., Poline, J., Thirion, B., Greicius, M. 2014: (in press)
  • Modeling Brain Activation in fMRI Using Group MRF IEEE TRANSACTIONS ON MEDICAL IMAGING Ng, B., Hamarneh, G., Abugharbieh, R. 2012; 31 (5): 1113-1123

    Abstract

    Noise confounds present serious complications to functional magnetic resonance imaging (fMRI) analysis. The amount of discernible signals within a single dataset of a subject is often inadequate to obtain satisfactory intra-subject activation detection. To remedy this limitation, we propose a novel group Markov random field (GMRF) that extends each subject's neighborhood system to other subjects to enable information coalescing. A distinct advantage of GMRF over standard fMRI group analysis is that no stringent one-to-one voxel correspondence is required. Instead, intra- and inter-subject neighboring voxels are jointly regularized to encourage spatially proximal voxels to be assigned similar labels across subjects. Our proposed group-extended graph structure thus provides an effective means for handling inter-subject variability. Also, adopting a group-wise approach by integrating group information into intra-subject activation, as opposed to estimating a single average group map, permits inter-subject differences to be characterized and studied. GMRF can be elegantly implemented as a single MRF, thus enabling all subjects' activation maps to be simultaneously and collaboratively segmented with global optimality guaranteed in the case of binary labeling. We validate our technique on synthetic and real fMRI data and demonstrate GMRF's superior performance over standard fMRI analysis.

    View details for DOI 10.1109/TMI.2012.2185943

    View details for Web of Science ID 000303502400011

    View details for PubMedID 22287237

  • Group Replicator Dynamics: A Novel Group-Wise Evolutionary Approach for Sparse Brain Network Detection IEEE TRANSACTIONS ON MEDICAL IMAGING Bernard Ng, B., McKeown, M. J., Abugharbieh, R. 2012; 31 (3): 576-585
  • Focusing Effects of L-Dopa in Parkinson's Disease HUMAN BRAIN MAPPING Ng, B., Palmer, S., Abugharbieh, R., McKeown, M. J. 2010; 31 (1): 88-97

    Abstract

    Previous fMRI motor studies in Parkinson's disease (PD) have suggested that L-dopa may "normalize" areas of hypo- and hyperactivity. However, results from these studies, which were largely based on analyzing BOLD signal amplitude, have been conflicting. Examining only amplitude changes at distinct loci may thus be inadequate in fully capturing the activation changes induced by L-dopa. In this article, we extended prior analyses on the effects of L-dopa by investigating both amplitude and spatial changes of brain activation before and after L-dopa. Ten subjects with PD, both on and off medication, and ten healthy, age-matched controls performed a visuo-motor tracking task in which they sinusoidally squeezed a bulb at 0.25, 0.5, and 0.75 Hz. This task was contrasted with static squeezing to generate fMRI activation maps. To investigate the effects of L-dopa, we examined the amplitude and spatial variance of the BOLD response within anatomically-defined regions of interest (ROIs). L-dopa had significant main effects on the amplitude of BOLD signal in bilateral primary motor cortex and left SMA. In contrast, L-dopa-mediated spatial changes were apparent in bilateral cerebellar hemispheres, M1, SMA, and right prefrontal cortex. Moreover, L-dopa appeared to normalize the spatial distribution of ROI activation in PD to that of the controls. Specifically, L-dopa had a "focusing" effect on activity-an effect more pronounced than the typically-measured fMRI amplitude changes. This observation is consistent with modeling studies, which demonstrated that dopamine increases the signal-to-noise ratio at the neuronal level with a resultant focusing of representations at the macroscopic level.

    View details for DOI 10.1002/hbm.20847

    View details for Web of Science ID 000273544700008

    View details for PubMedID 19585587

  • Motor reserve and novel area recruitment: amplitude and spatial characteristics of compensation in Parkinson's disease EUROPEAN JOURNAL OF NEUROSCIENCE Palmer, S. J., Ng, B., Abugharbieh, R., Eigenraam, L., McKeown, M. J. 2009; 29 (11): 2187-2196

    Abstract

    Motor symptoms of Parkinson's disease (PD) do not appear until the majority of dopaminergic cells in the substantia nigra pars compacta are lost, suggesting significant redundancy or compensation in the motor systems affected by PD. Using functional magnetic resonance imaging, we examined whether compensation in PD is manifested by changes in amplitude and/or spatial extent of activity within normal networks (active motor reserve) and/or newly recruited regions [novel area recruitment (NAR)]. Ten PD subjects off and on medication and 10 age-matched controls performed a visually guided sinusoidal force task at 0.25, 0.5 and 0.75 Hz. Regression was used to determine the combination of regions where activation amplitude scaled linearly with movement speed in controls. We then determined the activation of PD subjects in this network, as well as the corresponding PD network. To measure the spatial variance of activation, we used an invariant spatial feature approach. Control subjects monotonically increased activity within striato-thalamo-cortical and cerebello-thalamo-cortical regions with increasing movement speed. In PD subjects, the activity of this network at low speeds was similar to that in controls at higher speeds. Additionally, PD subjects off medication demonstrated NARs of the bilateral cerebellum and primary motor cortex, which were incompletely normalized by levodopa. Our results suggest that PD subjects tap into motor reserve, increase the spatial extent of activation and demonstrate NAR to maintain near-normal motor output.

    View details for DOI 10.1111/j.1460-9568.2009.06753.x

    View details for Web of Science ID 000266597000009

    View details for PubMedID 19490021

  • Spatial Characterization of fMRI Activation Maps Using Invariant 3D Moment Descriptors IEEE TRANSACTIONS ON MEDICAL IMAGING Ng, B., Abugharbieh, R., Huang, X., McKeown, M. J. 2009; 28 (2): 261-268

Books and Book Chapters


  • Shape Analysis for Brain Structures Advances of Shape Analysis in Medical Image Analysis Ng, B., Toews, M., Durrleman, S., Shi, Y. Springer. 2014: 3-49

Conference Proceedings


  • Effects of Tractography Approach on Consistency between Anatomical and Functional Connectivity Estimates International Symposium on Biomedical Imaging (ISBI) Yoldemir, B., Ng, B., Abugharbieh, R. 2014: 250−253
  • Transport on Riemannian Manifold for Functional Connectivity-based Classification International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Dresler, M., Varoquaux, G., Poline, J., Greicius, M., Thirion, B. 2014: 405−413
  • A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation International Conference on Information Processing in Medical Imaging (IPMI) Ng, B., Varoquaux, G., Poline, J., Thirion, B. 2013: 256−267
  • Recent Advances in Supervised Learning for Brain Graph Classification IEEE Global Conference on Signal and Information Processing (GlobalSIP) Richiardi, J., Ng, B. 2013: 907-910
  • Overlapping Replicator Dynamics for Functional Subnetwork Identification International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Yoldemir, B., Ng, B., Abugharbieh, R. 2013: 682−689
  • Fiber Connectivity Integrated Brain Activation Detection International Conference on Information Processing in Medical Imaging (IPMI) Yoldemir, B., Ng, B., Woodward, T., Abugharbieh, R. 2013: 135−146
  • Implications of Inconsistencies between fMRI and dMRI on Multimodal Connectivity Estimation International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Varoquaux, G., Poline, J., Thirion, B. 2013: 652−659
  • Connectivity-informed Sparse Classifiers for fMRI Brain Decoding International Workshop on Pattern Recognition in NeuroImaging (PRNI) Ng, B., Siless, V., Varoquaux, G., Poline, J., Abugharbieh, R., Thirion, B. 2012: 101-104
  • Deconfounding the Effects of Resting State Activity on Task Activation Detection in fMRI Workshop on Multimodal Brain Image Analysis held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Yoldemir, B., Ng, B., Abugharbieh, R. 2012: 51-60
  • A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Varoquaux, G., Poline, J., Thirion, B. 2012: 706−713
  • Connectivity-informed fMRI Activation Detection International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Abugharbieh, R., Varoquaux, G., Poline, J., Thirion, B. 2011: 285−292
  • Generalized Sparse Regularization with Application to fMRI Brain Decoding International Conference on Information Processing in Medical Imaging (IPMI) Ng, B., Abugharbieh, R. 2011: 612−623
  • Generalized Group Sparse Classifiers with Application in fMRI Brain Decoding International Conference on Computer Vision and Pattern Recognition (CVPR) Ng, B., Abugharbieh, R. 2011: 1065−1071
  • Modeling Spatiotemporal Structure in fMRI Brain Decoding Using Generalized Sparse Classifier International Workshop on Pattern Recognition in NeuroImaging (PRNI) Ng, B., Abugharbieh, R. 2011: 65-68
  • Generalized Sparse Classifiers for Decoding Cognitive States in fMRI Workshop on Machine Learning in Medical Imaging held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Vahdat, A., Hamarneh, G., Abugharbieh, R. 2010: 108−115
  • Group MRF for fMRI Activation Detection International Conference on Computer Vision and Pattern Recognition (CVPR) Ng, B., Abugharbieh, R., Hamarneh, G. 2010: 2887−2894
  • Detecting Brain Activation in fMRI Using Group Random Walker Internation Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Hamarneh, G., Abugharbieh, R. 2010: 331−338
  • Adverse Effects of Template-based Warping on Spatial fMRI Analysis International Conference on Medical Imaging organized by SPIE Ng, B., Abugharbieh, R., McKeown, M. J. 2009: 2621Y−12 pages
  • Random Walker Based Estimation and Spatial Analysis of Probabilistic fMRI Activation Maps Workshop on fMRI Data Analysis held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Abugharbieh, R., Hamarneh, G., McKeown, M. J. 2009: 37-44
  • Detecting Maximal Directional Changes in Spatial fMRI Response Using Canonical Correlation Analysis International Symposium on Biomedical Imaging (ISBI) Ng, B., Abugharbieh, R., McKeown, M. J. 2009: 650−653
  • Functional Segmentation of fMRI Data Using Adaptive Non-negative Sparse PCA (ANSPCA) International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Abugharbieh, R., McKeown, M. J. 2009: 490−497
  • Discovering Sparse Functional Brain Networks Using Group Replicator Dynamics International Conference on Information Processing in Medical Imaging (IPMI) Ng, B., Abugharbieh, R., McKeown, M. J. 2009: 76−87
  • Inferring Functional Connectivity Using Spatial Modulation Measures of fMRI Signals within Brain Regions of Interest International Symposium on Biomedical Imaging (ISBI) Ng, B., Abugharbieh, R., McKeown, M. J. 2008: 572−575
  • Enhanced fMRI Response Detection and Reduced Latency through Spatial Analysis of BOLD Signals Workshop on Analysis of Functional Medical Images held in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Abugharbieh, R., McKeown, M. J. 2008: 81-88
  • Joint Spatial Denoising and Active Region of Interest Delineation in Functional Magnetic Resonance Imaging Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Ng, B., Abugharbieh, R., Palmer, S. J., McKeown, M. J. 2007: 3404−3407
  • Characterizing Task-Related Temporal Dynamics of Spatial Activation Distributions in fMRI BOLD Signals International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Ng, B., Abugharbieh, R., Palmer, S. J., McKeown, M. J. 2007: 767−774
  • Characterizing fMRI Activations within Regions of Interest (ROIs) Using 3D Moment Invariants Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) held in conjunction with International Conference on Computer Vision and Pattern Recognition (CVPR) Ng, B., Abugharbieh, R., Huang, X., McKeown, M. J. 2006: 63-8 pages
  • Learning Feedforward Commands to Muscles Using Time-shifted Sensory Feedback International Conference on Brain-inspired Information Technology Milner, T. E., Ng, B., Franklin, D. W. 2005: 113-116

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