Ph.D. Student, Department of Electrical and Computer Engineering
Johns Hopkins University
[1] “Genetic influences on brain structure”, OHBM-2010, Barcelona.
LATEST PROJECTS
G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, while preserving interpretability. Our model consists of an encoder, a decoder and a classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional encoding captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we demonstrate that our model achieves better classification accuracy than baseline methods, and that this performance generalizes to a second dataset collected at a different site. The biomarkers identified by our model are closely associated with the well-documented deficits in schizophrenia.
Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework
We propose a joint dictionary learning framework that couples imaging and genetics data in a low dimensional subspace as guided by clinical diagnosis. We use a graph regularization penalty to simultaneously capture inter-regional brain interactions and identify the representative set anatomical basis vectors that span the low dimensional space. We further employ group sparsity to find the representative set of genetic basis vectors that span the same latent space. Finally, the latent projection is used to classify patients versus controls. We have evaluated our model on two task fMRI paradigms and single nucleotide polymorphism (SNP) data from schizophrenic patients and matched neurotypical controls. We employ a ten fold cross validation technique to show the predictive power of our model. We compare our model with canonical correlation analysis of imaging and genetics data and random forest classification. Our approach shows better prediction accuracy on both task datasets. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.
A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia
We present a generative-predictive framework that captures the differences in regional brain activity between a neurotypical cohort and a clinical population, as guided by patient-specific genetic risk. Our model assumes that the functional activations in the neurotypical subjects are distributed around a population mean, and that the altered brain activity in neuropsychiatric patients is defined via deviations from this neurotypical mean. We employ group sparsity to identify a set of brain regions that simultaneously explain the salient functional differences and specify a set of basis vector, that span the low dimensional data subspace. The patient-specific projections onto this subspace are used as feature vectors to identify multivariate associations with genetic risk. We have evaluated our model on a task-based fMRI dataset from a population study of schizophrenia. We compare our model with two baseline methods, regression using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression, which establishes direct association between the brain activity during a working memory task and schizophrenia polygenic risk. Our model demonstrates greater consistency and robustness across bootstrapping experiments than the machine learning baselines. Moreover, the set of brain regions implicated by our model underlie the well documented executive cognitive deficits in schizophrenia.
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Deep Deformable Image Registration
Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the registration pipeline. The upper bound SSD (UB-SSD) enhances the original deformable model parameter space by adding a heatmap output from FCNN. Next, we minimize this UB-SSD by adjusting both the parameters of the FCNN and the parameters of the deformable model in coordinate descent. Our coordinate descent framework is end-to-end and it can work with any deformable registration method that uses SSD. We demonstrate experimentally that our method enhances the accuracy of deformable registration algorithms significantly on two publicly available 3D brain MRI data sets.
Figure. Top left: A moving image slice; top right: a fixed image slice; bottom left: residual image from demons. bottom right: residual using DDR+demons.
A novel non-rigid registration algorithm for zebrafish larval images
Precise three-dimensional mapping of a large number of gene expression patterns, neuronal types and connections to an anatomical reference helps us to understand the vertebrate brain and its development. Zebrafish has evolved as a model organism for such study. In this paper, we propose a novel non-rigid registration algorithm for volumetric zebrafish larval image datasets. A coarse affine registration using the L-BFGS algorithm is applied first on the moving dataset. We then divide this coarsely registered moving image and the reference image into a union of overlapping patches. Minimum weight bipartite graph matching algorithm is employed to find the correspondence between the two sets of patches. The corresponding patches are then registered using the diffeomorphic demons method with proper intra-patch regularization. For each voxel lying in the overlapping regions, we impose inter-patch regularization through a composite transformation obtained from the adjacent transformation fields. Experimental results on four multi-view confocal 3D datasets show the advantage of the proposed solution over the existing ViBE-Z software.
Figure. Top Left: Moving Image, Top Right: Fixed Image, Bottom Left:
Registration using our method, Bottom Right: Registration using VibeZ. All implementations are done in 3D and all results are shown for the same 2D slice of dataset 2.
Figure. Experimental Setup and Electrode Positions
Robotic link position control using brain computer interface
Brain computer interfacing is a new paradigm to provide an alternative path of communication for those, who have lost their sensory, motor and autonomous function limb movement from spinal cord injury or other neuronal disorder. To increase living standard of them, a thought controlled robot or robotic arm can be of great importance for their better communication to external world. This paper proposes a novel approach towards electroencephalogram based random order control of a robot manipulator. Here steady state visual evoked potential has been used to select the desired robot link and motor imagery signal has been used to control the motion of robot link. Left and Right hand motor imagery signal has been used in this purpose. Event related error potential is used for feedback purpose and to correct the positional error of link created by motor imagery. The robot link here is realigned by experimental offset. Power spectral density, common spatial pattern and moving average, have been used as feature extractor and support vector machine with linear kernel function has been used as the classifier. At first, the subject has been trained in offline experiment and then online session performance has been evaluated.
Figure. Offline Stimuli Timing Diagram