Detecting Abnormal Functional Communities (2014-2016)
There is increasing evidence that complex pathologies reflect distributed impairments across multiple brain systems. Traditional network analyses in clinical neuroimaging are largely limited to aggregate statistics (e.g. centrality and degree distribution), which do not pinpoint a concrete etiological mechanism. We have developed a hierarchical Bayesian framework to detect both hyper- and hypo-synchronous functional subnetworks associated with a given experimental condition.
Our approach deviates from prior literature in three crucial ways. First, we propose a unified generative model that describes the relationship between population templates and individual subject observations. Second, we perform community detection in the space of group-level functional differences, which allows us to identify both enhanced and impaired processes. Finally, our framework can simultaneously detect multiple abnormal communities of varying type. We have applied the model to an fMRI study of social perception in autism, and we are currently evaluating its efficacy in predicting the effects of early behavioral intervention.
- A. Venkataraman, D. Yang, N. Dvornek, L.H. Staib, J.S. Duncan, K.A. Pelphrey, P. Ventola. Pivotal Response Treatment Prompts a Functional Rewiring of the Brain Among Individuals with Autism Spectrum Disorder. NeuroReport, 27(14):1081-1085, 2016.
- A. Venkataraman, D. Yang, K.A. Pelphrey, J.S. Duncan. Bayesian Community Detection in the Space of Group-Level Functional Differences. IEEE Transactions Medical Imaging, 35(8):1866-1882, 2016.
Identifying Foci of a Neurological Disorder (2011-2014)
Aberrations in functional connectivity inform us about disrupted neural communication, which may relate to certain brain pathologies. Although prior works have identified pairwise associations in the brain linked to a particular neurological disorder, such results are difficult to interpret and validate. This is because the bulk of our knowledge about the brain is organized around region properties (i.e., functional localization, tissue properties, morphometry) and not the connections between them.
We develop a unified framework that pinpoints region “foci”, whose connectivity patterns are most disrupted by the disorder. Using a probabilistic setting, we define a latent graph that characterizes the network of abnormal functional connectivity emanating from the affected foci. This generates population differences in the observed fMRI correlations. We present two versions of the model. The first variant considers the complete graph of pairwise functional connections. The second model uses neural anatomy as a substrate for modeling functional differences.
We have validated the models on a population study of schizophrenia, Alzheimer’s disease and traumatic brain injury. We have also adapted this framework for subject-specific analysis in order to identify the origins of epileptic seizures in the brain, and we have extended the model to extract multiple networks via a clustering prior. This modification has allowed us to disentangle social and language deficits in autism.
- D. Rangaprakash, M.N. Dretsch, A. Venkataraman, J.S. Katz, T.S. Denney Jr., G. Deshpande. Identifying Disease Foci from Static and Dynamic Effective Connectivity Networks: Illustration in Soldiers with Trauma. Human Brain Mapping, 39(1):264-287, 2018.
- S. Zhao, D. Rangaprakash, A. Venkataraman, P. Liang, G. Deshpande. Investigating Focal Connectivity Deficits in Alzheimer’s Disease using Directional Brain Networks Derived from Resting-State fMRI. Frontiers on Aging Neuroscience, 9:1-12, 2017.
- A. Venkataraman, J.S. Duncan, D. Yang, K.A. Pelphrey. An Unbiased Bayesian Approach to Functional Connectomics Implicates Social-Communication Networks in Autism. NeuroImage Clinical, 8:356-366, 2015.
- A. Venkataraman, M. Kubicki, P. Golland. From Brain Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder. IEEE Transactions on Medical Imaging, 32(11):2078-2098, 2013.
Joint Modeling of fMRI and DWI Data (2009-2011)
The interaction between functional and anatomical imaging modalities provides a rich framework for understanding the brain. In particular, correlations present in resting-state fMRI data are believed to reflect the intrinsic functional organization between regions. Similarly, tractography based on DWI data is used to estimate underlying white matter fiber tracts (anatomical pathways). However, relatively little progress has been made in fusing the complementary information provided by these modalities.
In this work, we propose a novel probabilistic framework to infer the relationship between fMRI and DWI data. The model is based on latent connectivity templates and makes intuitive assumptions about the data generation process. Additionally, we formulate a natural extension of the model to population studies.
- A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin, P. Golland. Joint Modeling of Anatomical and Functional Connectivity for Population Studies. IEEE Transactions on Medical Imaging, 31(2):164-182, 2012.
- A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin, P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS 6361:191-199, 2010.
Extracting Robust fMRI Measures for Clinical Diagnosis (2009-2010)
Pinpointing robust differences between a control and clinical population is crucial for understanding the effects of a disease. This task is especially difficult for resting-state fMRI data due to the considerable amount of noise/inter-subject variability and to the relatively small number of subjects found in clinical experiments. Univariate tests and random effects analysis are the standard techniques for population studies. However, this analysis ignores multivariate patterns within the data and is not robust.
Our approach is to apply feature selection/classification algorithms to identify predictive differences between the populations. We successfully employed the Gini Importance measure derived from Random Forests to identify discriminative functional connectivity anomalies induced by schizophrenia.
- A. Venkataraman, T.J. Whitford, C-F. Westin, P. Golland, M. Kubicki. Whole Brain Resting State Functional Connectivity Abnormalities in Schizophrenia. Schizophrenia Research, 139(1-3):7-12, 2012.
- A. Venkataraman, M. Kubicki, C-F. Westin, P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis: 63-70, 2010.
Data-Driven Functional Connectivity Analysis (2008-2009)
We investigate the use of data-driven clustering algorithms as complementary approaches to seed-based analysis. This allows us to partition the whole brain into an increasing number of clusters. We show that both K-Means and Spectral Clustering yield patterns that correspond to well-known functional systems.
- K.R.A. Van Dijk, T. Hedden, A. Venkataraman, K.C. Evans, S.W. Lazar, R.L. Buckner. Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization. J Neurophysiology, 103(1):297-321, 2010.
- A. Venkataraman, K.R.A Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. In Proc. ICASSP: IEEE Conference on Acoustics, Speech and Signal Proc, 441-444, 2009.
- P. Golland, D. Lashkari, A. Venkataraman. Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data. In Proc. Asilomar Conf on Signals, Systems and Computers, 1402-1409, 2008.