Bio

Honors & Awards


  • Senior Member, IEEE (2009-)

Professional Education


  • PhD, Indian Institute of Science, Electrical Engineering (2005)
  • M S, Indian Institute of Science, Electrical Engineering (1998)
  • BS, Osmania University, Electrical Engineering (1996)

Publications

Journal Articles


  • A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI NEUROIMAGE Ryali, S., Chen, T., Supekar, K., Menon, V. 2013; 65: 83-96

    Abstract

    Understanding the organization of the human brain requires identification of its functional subdivisions. Clustering schemes based on resting-state functional magnetic resonance imaging (fMRI) data are rapidly emerging as non-invasive alternatives to cytoarchitectonic mapping in postmortem brains. Here, we propose a novel spatio-temporal probabilistic parcellation scheme that overcomes major weaknesses of existing approaches by (i) modeling the fMRI time series of a voxel as a von Mises-Fisher distribution, which is widely used for clustering high dimensional data; (ii) modeling the latent cluster labels as a Markov random field, which provides spatial regularization on the cluster labels by penalizing neighboring voxels having different cluster labels; and (iii) introducing a prior on the number of labels, which helps in uncovering the number of clusters automatically from the data. Cluster labels and model parameters are estimated by an iterative expectation maximization procedure wherein, given the data and current estimates of model parameters, the latent cluster labels, are computed using ?-expansion, a state of the art graph cut, method. In turn, given the current estimates of cluster labels, model parameters are estimated by maximizing the pseudo log-likelihood. The performance of the proposed method is validated using extensive computer simulations. Using novel stability analysis we examine the sensitivity of our methods to parameter initialization and demonstrate that the method is robust to a wide range of initial parameter values. We demonstrate the application of our methods by parcellating spatially contiguous as well as non-contiguous brain regions at both the individual participant and group levels. Notably, our analyses yield new data on the posterior boundaries of the supplementary motor area and provide new insights into functional organization of the insular cortex. Taken together, our findings suggest that our method is a powerful tool for investigating functional subdivisions in the human brain.

    View details for DOI 10.1016/j.neuroimage.2012.09.067

    View details for Web of Science ID 000312283900008

    View details for PubMedID 23041530

  • Multivariate dynamical systems models for estimating causal interactions in fMRI NEUROIMAGE Ryali, S., Supekar, K., Chen, T., Menon, V. 2011; 54 (2): 807-823

    Abstract

    Analysis of dynamical interactions between distributed brain areas is of fundamental importance for understanding cognitive information processing. However, estimating dynamic causal interactions between brain regions using functional magnetic resonance imaging (fMRI) poses several unique challenges. For one, fMRI measures Blood Oxygenation Level Dependent (BOLD) signals, rather than the underlying latent neuronal activity. Second, regional variations in the hemodynamic response function (HRF) can significantly influence estimation of causal interactions between them. Third, causal interactions between brain regions can change with experimental context over time. To overcome these problems, we developed a novel state-space Multivariate Dynamical Systems (MDS) model to estimate intrinsic and experimentally-induced modulatory causal interactions between multiple brain regions. A probabilistic graphical framework is then used to estimate the parameters of MDS as applied to fMRI data. We show that MDS accurately takes into account regional variations in the HRF and estimates dynamic causal interactions at the level of latent signals. We develop and compare two estimation procedures using maximum likelihood estimation (MLE) and variational Bayesian (VB) approaches for inferring model parameters. Using extensive computer simulations, we demonstrate that, compared to Granger causal analysis (GCA), MDS exhibits superior performance for a wide range of signal to noise ratios (SNRs), sample length and network size. Our simulations also suggest that GCA fails to uncover causal interactions when there is a conflict between the direction of intrinsic and modulatory influences. Furthermore, we show that MDS estimation using VB methods is more robust and performs significantly better at low SNRs and shorter time series than MDS with MLE. Our study suggests that VB estimation of MDS provides a robust method for estimating and interpreting causal network interactions in fMRI data.

    View details for DOI 10.1016/j.neuroimage.2010.09.052

    View details for Web of Science ID 000285486000009

    View details for PubMedID 20884354

  • Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. Journal of Neuroscience Lucina Uddin, K., Srikanth Ryali, Vinod Menon 2011
  • Sparse logistic regression for whole-brain classification of fMRI data NEUROIMAGE Ryali, S., Supekar, K., Abrams, D. A., Menon, V. 2010; 51 (2): 752-764

    Abstract

    Multivariate pattern recognition methods are increasingly being used to identify multiregional brain activity patterns that collectively discriminate one cognitive condition or experimental group from another, using fMRI data. The performance of these methods is often limited because the number of regions considered in the analysis of fMRI data is large compared to the number of observations (trials or participants). Existing methods that aim to tackle this dimensionality problem are less than optimal because they either over-fit the data or are computationally intractable. Here, we describe a novel method based on logistic regression using a combination of L1 and L2 norm regularization that more accurately estimates discriminative brain regions across multiple conditions or groups. The L1 norm, computed using a fast estimation procedure, ensures a fast, sparse and generalizable solution; the L2 norm ensures that correlated brain regions are included in the resulting solution, a critical aspect of fMRI data analysis often overlooked by existing methods. We first evaluate the performance of our method on simulated data and then examine its effectiveness in discriminating between well-matched music and speech stimuli. We also compared our procedures with other methods which use either L1-norm regularization alone or support vector machine-based feature elimination. On simulated data, our methods performed significantly better than existing methods across a wide range of contrast-to-noise ratios and feature prevalence rates. On experimental fMRI data, our methods were more effective in selectively isolating a distributed fronto-temporal network that distinguished between brain regions known to be involved in speech and music processing. 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.

    View details for DOI 10.1016/j.neuroimage.2010.02.040

    View details for Web of Science ID 000277141200026

    View details for PubMedID 20188193

  • Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings NEUROIMAGE Ryali, S., Glover, G. H., Chang, C., Menon, V. 2009; 48 (2): 348-361

    Abstract

    EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on independent component analysis (ICA) for reducing gradient artifacts from spiral in-out and echo-planar pulse sequences at 3 T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen, P., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230-239.), principal component analysis (Niazy, R., Beckmann, C., Iannetti, G., Brady, J., Smith, S., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720-737.), Taylor series ( Wan, X., Iwata, K., Riera, J., Kitamura, M., Kawashima, R., 2006. Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts. Clin. Neurophysiol. 117, 681-692.) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (<12.5 Hz) with high signal-to-noise ratio (SNR>4). Our algorithms outperformed all of these methods on resting EEG in the theta and alpha bands (SNR>4); however, for all methods, signal recovery was modest (SNR approximately 1) in the beta band and poor (SNR<0.3) in the gamma band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (<0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3 T using spiral in-out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework.

    View details for DOI 10.1016/j.neuroimage.2009.06.072

    View details for Web of Science ID 000274723900005

    View details for PubMedID 19580873

  • Biological parametric mapping: A statistical toolbox for multimodality brain image analysis NEUROIMAGE Casanova, R., Srikanth, R., Baer, A., Laurienti, P. J., Burdett, J. H., Hayasaka, S., Flowers, L., Wood, F., Maldjian, J. A. 2007; 34 (1): 137-143

    Abstract

    In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. An example with in vivo data is presented, demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.

    View details for DOI 10.1016/j.neuroimage.2006.09.011

    View details for Web of Science ID 000242735300014

    View details for PubMedID 17070709

  • Estimation of false discovery rates for wavelet-denoised statistical parametric maps NEUROIMAGE Srikanth, R., Casanova, R., Laurienti, P. J., Peiffer, A. M., Maldjian, J. A. 2006; 33 (1): 72-84

    Abstract

    Correction for multiple comparisons in neuroimaging data is an important area of research. Recently, wavelet-based methods have gained popularity and have been reported to achieve better sensitivity compared to spatial domain methods. However, these techniques produce smoothed statistical maps which are difficult to interpret. The generated maps have to be thresholded again in the spatial domain to delineate active from inactive regions. The selection of a proper threshold satisfying the required error rate control is not straightforward. In this paper, a framework is proposed for thresholding wavelet-denoised maps in which a rejection region is fixed, and the achieved false discovery rate (FDR) is estimated. This approach provides a meaningful strategy to choose thresholds for wavelet-denoised statistical parametric maps (SPMs). Two FDR estimation algorithms were used to assess the achieved error rate control when thresholding wavelet filtered SPMs at various rejection regions. Their performance was evaluated using both simulated and resting fMRI data. The proposed framework was also applied on in vivo data.

    View details for DOI 10.1016/j.neuroimage.2006.06.033

    View details for Web of Science ID 000241209800010

    View details for PubMedID 16919480

  • Wavelet-based estimation of hemodynamic response function from fMRI data INTERNATIONAL JOURNAL OF NEURAL SYSTEMS Srikanth, R., Ramakrshnan, A. G. 2006; 16 (2): 125-138

    Abstract

    We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm). The HRF parameters are estimated in wavelet domain by exploiting the property that wavelet transforms with a sufficient number of vanishing moments decorrelates a fBm process. Using this property, the noise covariance matrix in wavelet domain can be assumed to be diagonal whose entries are estimated using the sample variance estimator at each scale. We study the influence of the sampling rate of fMRI time series and shape assumption of HRF on the estimation performance. Results are presented by adding synthetic HRFs on simulated and null fMRI data. We also compare these methods with an existing method,(1) where correlated fMRI noise is modeled by a second order polynomial functions.

    View details for Web of Science ID 000237787200004

    View details for PubMedID 16688852

  • Contextual encoding in uniform and adaptive mesh-based lossless compression of MR images IEEE TRANSACTIONS ON MEDICAL IMAGING Srikanth, R., Ramakrishnan, A. G. 2005; 24 (9): 1199-1206

    Abstract

    We propose and evaluate a number of novel improvements to the mesh-based coding scheme for 3-D brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing of only the brain part of the image; 2) content-based (adaptive) mesh generation using spatial edges and optical flow between two consecutive slices; 3) a simple solution for the aperture problem at the edges, where an accurate estimation of motion vectors is not possible; and 4) context-based entropy coding of the residues after motion compensation using affine transformations. We address only lossless coding of the images, and compare the performance of uniform and adaptive mesh-based schemes. The bit rates achieved (about 2 bits per voxel) by these schemes are comparable to those of the state-of-the-art three-dimensional (3-D) wavelet-based schemes. The mesh-based schemes have been shown to be effective for the compression of 3-D brain computed tomography data also. Adaptive mesh-based schemes perform marginally better than the uniform mesh-based methods, at the expense of increased complexity.

    View details for DOI 10.1109/TMI.2005.853638

    View details for Web of Science ID 000231636400010

    View details for PubMedID 16156357

  • Estimation of resting-state functional connectivity using random subspace based partial correlation: A novel method for reducing global artifacts. NeuroImage Chen, T., Ryali, S., Qin, S., Menon, V. 2013; 82: 87-100

    Abstract

    Intrinsic functional connectivity analysis using resting-state functional magnetic resonance imaging (rsfMRI) has become a powerful tool for examining brain functional organization. Global artifacts such as physiological noise pose a significant problem in estimation of intrinsic functional connectivity. Here we develop and test a novel random subspace method for functional connectivity (RSMFC) that effectively removes global artifacts in rsfMRI data. RSMFC estimates the partial correlation between a seed region and each target brain voxel using multiple subsets of voxels sampled randomly across the whole brain. We evaluated RSMFC on both simulated and experimental rsfMRI data and compared its performance with standard methods that rely on global mean regression (GSReg) which are widely used to remove global artifacts. Using extensive simulations we demonstrate that RSMFC is effective in removing global artifacts in rsfMRI data. Critically, using a novel simulated dataset we demonstrate that, unlike GSReg, RSMFC does not artificially introduce anti-correlations between inherently uncorrelated networks, a result of paramount importance for reliably estimating functional connectivity. Furthermore, we show that the overall sensitivity, specificity and accuracy of RSMFC are superior to GSReg. Analysis of posterior cingulate cortex connectivity in experimental rsfMRI data from 22 healthy adults revealed strong functional connectivity in the default mode network, including more reliable identification of connectivity with left and right medial temporal lobe regions that were missed by GSReg. Notably, compared to GSReg, negative correlations with lateral fronto-parietal regions were significantly weaker in RSMFC. Our results suggest that RSMFC is an effective method for minimizing the effects of global artifacts and artificial negative correlations, while accurately recovering intrinsic functional brain networks.

    View details for DOI 10.1016/j.neuroimage.2013.05.118

    View details for PubMedID 23747287

  • Multivariate searchlight classification of structural MRI in children and adolescents with autism. Biological Psychiatry Uddin, L. Q, Menon, V., Young, C. B., Ryali, S., Chen, T., Khouzam, A., Minshew, N. J. & Hardan
  • Salience Network-Based Classification and Prediction of Symptom Severity in Children With Autism JAMA PSYCHIATRY Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., Menon, V. 2013; 70 (8): 869-879

    Abstract

    IMPORTANCE Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood. OBJECTIVES To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD. DESIGN, SETTING, AND PARTICIPANTS Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children. MAIN OUTCOMES AND MEASURES Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD. RESULTS We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual's salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen-level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity. CONCLUSIONS AND RELEVANCE Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD.

    View details for DOI 10.1001/jamapsychiatry.2013.104

    View details for Web of Science ID 000322833600013

    View details for PubMedID 23803651

  • Underconnectivity between voice-selective cortex and reward circuitry in children with autism PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Abrams, D. A., Lynch, C. J., Cheng, K. M., Phillips, J., Supekar, K., Ryali, S., Uddin, L. Q., Menon, V. 2013; 110 (29): 12060-12065

    Abstract

    Individuals with autism spectrum disorders (ASDs) often show insensitivity to the human voice, a deficit that is thought to play a key role in communication deficits in this population. The social motivation theory of ASD predicts that impaired function of reward and emotional systems impedes children with ASD from actively engaging with speech. Here we explore this theory by investigating distributed brain systems underlying human voice perception in children with ASD. Using resting-state functional MRI data acquired from 20 children with ASD and 19 age- and intelligence quotient-matched typically developing children, we examined intrinsic functional connectivity of voice-selective bilateral posterior superior temporal sulcus (pSTS). Children with ASD showed a striking pattern of underconnectivity between left-hemisphere pSTS and distributed nodes of the dopaminergic reward pathway, including bilateral ventral tegmental areas and nucleus accumbens, left-hemisphere insula, orbitofrontal cortex, and ventromedial prefrontal cortex. Children with ASD also showed underconnectivity between right-hemisphere pSTS, a region known for processing speech prosody, and the orbitofrontal cortex and amygdala, brain regions critical for emotion-related associative learning. The degree of underconnectivity between voice-selective cortex and reward pathways predicted symptom severity for communication deficits in children with ASD. Our results suggest that weak connectivity of voice-selective cortex and brain structures involved in reward and emotion may impair the ability of children with ASD to experience speech as a pleasurable stimulus, thereby impacting language and social skill development in this population. Our study provides support for the social motivation theory of ASD.

    View details for DOI 10.1073/pnas.1302982110

    View details for Web of Science ID 000322086100085

    View details for PubMedID 23776244

  • Multivariate Activation and Connectivity Patterns Discriminate Speech Intelligibility in Wernicke's, Broca's, and Geschwind's Areas CEREBRAL CORTEX Abrams, D. A., Ryali, S., Chen, T., Balaban, E., Levitin, D. J., Menon, V. 2013; 23 (7): 1703-1714

    Abstract

    The brain network underlying speech comprehension is usually described as encompassing fronto-temporal-parietal regions while neuroimaging studies of speech intelligibility have focused on a more spatially restricted network dominated by the superior temporal cortex. Here we use functional magnetic resonance imaging with a novel whole-brain multivariate pattern analysis (MVPA) to more fully characterize neural responses and connectivity to intelligible speech. Consistent with previous univariate findings, intelligible speech elicited greater activity in bilateral superior temporal cortex relative to unintelligible speech. However, MVPA identified a more extensive network that discriminated between intelligible and unintelligible speech, including left-hemisphere middle temporal gyrus, angular gyrus, inferior temporal cortex, and inferior frontal gyrus pars triangularis. These fronto-temporal-parietal areas also showed greater functional connectivity during intelligible, compared with unintelligible, speech. Our results suggest that speech intelligibly is encoded by distinct fine-grained spatial representations and within-task connectivity, rather than differential engagement or disengagement of brain regions, and they provide a more complete view of the brain network serving speech comprehension. Our findings bridge a divide between neural models of speech comprehension and the neuroimaging literature on speech intelligibility, and suggest that speech intelligibility relies on differential multivariate response and connectivity patterns in Wernicke's, Broca's, and Geschwind's areas.

    View details for DOI 10.1093/cercor/bhs165

    View details for Web of Science ID 000321163700020

    View details for PubMedID 22693339

  • Inter-subject synchronization of brain responses during natural music listening. European journal of neuroscience Abrams, D. A., Ryali, S., Chen, T., Chordia, P., Khouzam, A., Levitin, D. J., Menon, V. 2013; 37 (9): 1458-1469

    Abstract

    Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic 'real-world' music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and spectral structure of the Natural Music condition were disrupted, to non-musician participants undergoing functional brain imaging and analysing synchronized spatiotemporal activity patterns between listeners. We found that music synchronizes brain responses across listeners in bilateral auditory midbrain and thalamus, primary auditory and auditory association cortex, right-lateralized structures in frontal and parietal cortex, and motor planning regions of the brain. These effects were greater for natural music compared to the pseudo-musical control conditions. Remarkably, inter-subject synchronization in the inferior colliculus and medial geniculate nucleus was also greater for the natural music condition, indicating that synchronization at these early stages of auditory processing is not simply driven by spectro-temporal features of the stimulus. Increased synchronization during music listening was also evident in a right-hemisphere fronto-parietal attention network and bilateral cortical regions involved in motor planning. While these brain structures have previously been implicated in various aspects of musical processing, our results are the first to show that these regions track structural elements of a musical stimulus over extended time periods lasting minutes. Our results show that a hierarchical distributed network is synchronized between individuals during the processing of extended musical sequences, and provide new insight into the temporal integration of complex and biologically salient auditory sequences.

    View details for DOI 10.1111/ejn.12173

    View details for PubMedID 23578016

  • Hippocampal-Prefrontal Engagement and Dynamic Causal Interactions in the Maturation of Children's Fact Retrieval JOURNAL OF COGNITIVE NEUROSCIENCE Cho, S., Metcalfe, A. W., Young, C. B., Ryali, S., Geary, D. C., Menon, V. 2012; 24 (9): 1849-1866

    Abstract

    Children's gains in problem-solving skills during the elementary school years are characterized by shifts in the mix of problem-solving approaches, with inefficient procedural strategies being gradually replaced with direct retrieval of domain-relevant facts. We used a well-established procedure for strategy assessment during arithmetic problem solving to investigate the neural basis of this critical transition. We indexed behavioral strategy use by focusing on the retrieval frequency and examined changes in brain activity and connectivity associated with retrieval fluency during arithmetic problem solving in second- and third-grade (7- to 9-year-old) children. Children with higher retrieval fluency showed elevated signal in the right hippocampus, parahippocampal gyrus (PHG), lingual gyrus (LG), fusiform gyrus (FG), left ventrolateral PFC (VLPFC), bilateral dorsolateral PFC (DLPFC), and posterior angular gyrus. Critically, these effects were not confounded by individual differences in problem-solving speed or accuracy. Psychophysiological interaction analysis revealed significant effective connectivity of the right hippocampus with bilateral VLPFC and DLPFC during arithmetic problem solving. Dynamic causal modeling analysis revealed strong bidirectional interactions between the hippocampus and the left VLPFC and DLPFC. Furthermore, causal influences from the left VLPFC to the hippocampus served as the main top-down component, whereas causal influences from the hippocampus to the left DLPFC served as the main bottom-up component of this retrieval network. Our study highlights the contribution of hippocampal-prefrontal circuits to the early development of retrieval fluency in arithmetic problem solving and provides a novel framework for studying dynamic developmental processes that accompany children's development of problem-solving skills.

    View details for Web of Science ID 000307045400004

    View details for PubMedID 22621262

  • Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty NEUROIMAGE Ryali, S., Chen, T., Supekar, K., Menon, V. 2012; 59 (4): 3852-3861

    Abstract

    Characterizing interactions between multiple brain regions is important for understanding brain function. Functional connectivity measures based on partial correlation provide an estimate of the linear conditional dependence between brain regions after removing the linear influence of other regions. Estimation of partial correlations is, however, difficult when the number of regions is large, as is now increasingly the case with a growing number of large-scale brain connectivity studies. To address this problem, we develop novel methods for estimating sparse partial correlations between multiple regions in fMRI data using elastic net penalty (SPC-EN), which combines L1- and L2-norm regularization We show that L1-norm regularization in SPC-EN provides sparse interpretable solutions while L2-norm regularization improves the sensitivity of the method when the number of possible connections between regions is larger than the number of time points, and when pair-wise correlations between brain regions are high. An issue with regularization-based methods is choosing the regularization parameters which in turn determine the selection of connections between brain regions. To address this problem, we deploy novel stability selection methods to infer significant connections between brain regions. We also compare the performance of SPC-EN with existing methods which use only L1-norm regularization (SPC-L1) on simulated and experimental datasets. Detailed simulations show that the performance of SPC-EN, measured in terms of sensitivity and accuracy is superior to SPC-L1, especially at higher rates of feature prevalence. Application of our methods to resting-state fMRI data obtained from 22 healthy adults shows that SPC-EN reveals a modular architecture characterized by strong inter-hemispheric links, distinct ventral and dorsal stream pathways, and a major hub in the posterior medial cortex - features that were missed by conventional methods. 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.

    View details for DOI 10.1016/j.neuroimage.2011.11.054

    View details for Web of Science ID 000301090100078

    View details for PubMedID 22155039

  • Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns CEREBRAL CORTEX SHIRER, W. R., RYALI, S., Rykhlevskaia, E., Menon, V., Greicius, M. D. 2012; 22 (1): 158-165

    Abstract

    Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.

    View details for DOI 10.1093/cercor/bhr099

    View details for Web of Science ID 000298190500014

    View details for PubMedID 21616982

  • Dynamic Reconfiguration of Structural and Functional Connectivity Across Core Neurocognitive Brain Networks with Development JOURNAL OF NEUROSCIENCE Uddin, L. Q., Supekar, K. S., Ryali, S., Menon, V. 2011; 31 (50): 18578-18589

    Abstract

    Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally oriented attention [central executive network (CEN)] and internally oriented cognition [default mode network (DMN)]. How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared with children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. Diffusion tensor imaging tractography revealed increased structural integrity in adults compared with children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood.

    View details for DOI 10.1523/JNEUROSCI.4465-11.2011

    View details for Web of Science ID 000298055600032

    View details for PubMedID 22171056

  • How does a child solve 7+8? Decoding brain activity patterns associated with counting and retrieval strategies DEVELOPMENTAL SCIENCE Cho, S., Ryali, S., Geary, D. C., Menon, V. 2011; 14 (5): 989-1001

    Abstract

    Cognitive development and learning are characterized by diminished reliance on effortful procedures and increased use of memory-based problem solving. Here we identify the neural correlates of this strategy shift in 7-9-year-old children at an important developmental period for arithmetic skill acquisition. Univariate and multivariate approaches were used to contrast brain responses between two groups of children who relied primarily on either retrieval or procedural counting strategies. Children who used retrieval strategies showed greater responses in the left ventrolateral prefrontal cortex; notably, this was the only brain region which showed univariate differences in signal intensity between the two groups. In contrast, multivariate analysis revealed distinct multivoxel activity patterns in bilateral hippocampus, posterior parietal cortex and left ventrolateral prefrontal cortex regions between the two groups. These results demonstrate that retrieval and counting strategies during early learning are characterized by distinct patterns of activity in a distributed network of brain regions involved in arithmetic problem solving and controlled retrieval of arithmetic facts. Our findings suggest that the reorganization and refinement of neural activity patterns in multiple brain regions plays a dominant role in the transition to memory-based arithmetic problem solving. Our findings further demonstrate how multivariate approaches can provide novel insights into fine-scale developmental changes in the brain. More generally, our study illustrates how brain imaging and developmental research can be integrated to investigate fundamental aspects of neurocognitive development.

    View details for DOI 10.1111/j.1467-7687.2011.01055.x

    View details for Web of Science ID 000294181600007

    View details for PubMedID 21884315

  • Decoding Temporal Structure in Music and Speech Relies on Shared Brain Resources but Elicits Different Fine-Scale Spatial Patterns CEREBRAL CORTEX Abrams, D. A., Bhatara, A., Ryali, S., Balaban, E., Levitin, D. J., Menon, V. 2011; 21 (7): 1507-1518

    Abstract

    Music and speech are complex sound streams with hierarchical rules of temporal organization that become elaborated over time. Here, we use functional magnetic resonance imaging to measure brain activity patterns in 20 right-handed nonmusicians as they listened to natural and temporally reordered musical and speech stimuli matched for familiarity, emotion, and valence. Heart rate variability and mean respiration rates were simultaneously measured and were found not to differ between musical and speech stimuli. Although the same manipulation of temporal structure elicited brain activation level differences of similar magnitude for both music and speech stimuli, multivariate classification analysis revealed distinct spatial patterns of brain responses in the 2 domains. Distributed neuronal populations that included the inferior frontal cortex, the posterior and anterior superior and middle temporal gyri, and the auditory brainstem classified temporal structure manipulations in music and speech with significant levels of accuracy. While agreeing with previous findings that music and speech processing share neural substrates, this work shows that temporal structure in the 2 domains is encoded differently, highlighting a fundamental dissimilarity in how the same neural resources are deployed.

    View details for DOI 10.1093/cercor/bhq198

    View details for Web of Science ID 000291750400005

    View details for PubMedID 21071617

  • Discriminatory Learning based Performance Monitoring of Batch Processes American Control Conference, USA Yelchuru, R., Srikanth Ryali, Patel, Shailesh, Gudi, Ravindra 2011
  • Aging and the Interaction of Sensory Cortical Function and Structure HUMAN BRAIN MAPPING Peiffer, A. M., Hugenschimidt, C. E., Maldjian, J. A., Casanova, R., Srikanth, R., Hayasaka, S., Burdette, J. H., Kraft, R. A., Laurienti, P. J. 2009; 30 (1): 228-240

    Abstract

    Even the healthiest older adults experience changes in cognitive and sensory function. Studies show that older adults have reduced neural responses to sensory information. However, it is well known that sensory systems do not act in isolation but function cooperatively to either enhance or suppress neural responses to individual environmental stimuli. Very little research has been dedicated to understanding how aging affects the interactions between sensory systems, especially cross-modal deactivations or the ability of one sensory system (e.g., audition) to suppress the neural responses in another sensory system cortex (e.g., vision). Such cross-modal interactions have been implicated in attentional shifts between sensory modalities and could account for increased distractibility in older adults. To assess age-related changes in cross-modal deactivations, functional MRI studies were performed in 61 adults between 18 and 80 years old during simple auditory and visual discrimination tasks. Results within visual cortex confirmed previous findings of decreased responses to visual stimuli for older adults. Age-related changes in the visual cortical response to auditory stimuli were, however, much more complex and suggested an alteration with age in the functional interactions between the senses. Ventral visual cortical regions exhibited cross-modal deactivations in younger but not older adults, whereas more dorsal aspects of visual cortex were suppressed in older but not younger adults. These differences in deactivation also remained after adjusting for age-related reductions in brain volume of sensory cortex. Thus, functional differences in cortical activity between older and younger adults cannot solely be accounted for by differences in gray matter volume.

    View details for DOI 10.1002/hbm.20497

    View details for Web of Science ID 000262397000020

    View details for PubMedID 18072271

  • The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis NEUROIMAGE Casanova, R., Ryali, S., Serences, J., Yang, L., Kraft, R., Laurienti, P. J., Maldjian, J. A. 2008; 40 (4): 1606-1618

    Abstract

    In fMRI data analysis it has been shown that for a wide range of situations the hemodynamic response function (HRF) can be reasonably characterized as the impulse response function of a linear and time invariant system. An accurate and robust extraction of the HRF is essential to infer quantitative information about the relative timing of the neuronal events in different brain regions. When no assumptions are made about the HRF shape, it is most commonly estimated using time windowed averaging or a least squares estimated general linear model based on either Fourier or delta basis functions. Recently, regularization methods have been employed to increase the estimation efficiency of the HRF; typically these methods produce more accurate HRF estimates than the least squares approach [Goutte, C., Nielsen, F.A., Hansen, L.K., 2000. Modeling the Haemodynamic Response in fMRI Using Smooth FIR Filters. IEEE Trans. Med. Imag. 19(12), 1188-1201.]. Here, we use simulations to clarify the relative merit of temporal regularization based methods compared to the least squares methods with respect to the accuracy of estimating certain characteristics of the HRF such as time to peak (TTP), height (HR) and width (W) of the response. We implemented a Bayesian approach proposed by Marrelec et al. [Marrelec, G., Benali, H., Ciuciu, P., Pelegrini-Issac, M., Poline, J.-B., 2003. Robust Estimation of the Hemodynamic Response Function in Event-Related BOLD fMRI Using Basic Physiological Information. Hum. Brain Mapp. 19, 1-17., Marrelec, G., Benali, H., Ciuciu, P., Poline, J.B. Bayesian estimation of the hemodynamic of the hemodynamic response function in functional MRI. In: R. F, editor; 2001; Melville. p 229-247.] and its deterministic counterpart based on a combination of Tikhonov regularization [Tikhonov, A.N., Arsenin, V.Y., 1977. Solution of ill-posed problems. Washington DC: W.H. Winston.] and generalized cross-validation (GCV) [Wahba, G., 1990. Spline Models for Observational Data. Philadelphia: SIAM.] for selecting the regularization parameter. The performance of both methods is compared with least square estimates as a function of temporal resolution, color and strength of the noise, and the type of stimulus sequences used. In almost all situations, under the considered assumptions (e.g. linearity, time invariance and smooth HRF), the regularization-based techniques more accurately characterize the HRF compared to the least-squares method. Our results clarify the effects of temporal resolution, noise color, and experimental design on the accuracy of HRF estimation.

    View details for DOI 10.1016/j.neuroimage.2008.01.011

    View details for Web of Science ID 000255347100018

    View details for PubMedID 18329292

  • Prediction of Batch Quality Indices Using Functional Space Approximation and Partial Least Squares American Control Conference, USA. Yelchuru, R., Patel, Shailesh, Srikanth Ryali, Gudi, Ravindra 2008
  • Polarization-rich continuous wave direct imaging: modeling and visualization Applied Optics R. S. Umesh, A. G. Ramakrishnan, R. Srikanth, R. Hema, S. Divya 2006; 45 (18): 4344-4354
  • Region of Interest Coding of 2-D and 3-D Magnetic Resonance Images ICVGIP Srikanth Ryali, A.G. Ramakrishnan 2004
  • Shape Adaptive Integer Wavelet Transform based coding scheme for 2-D/3-D MR Images Data Compression Conference, Snowbird, Utah, USA, Abhishek Mehrotra, Srikanth Ryali, A.G.Ramakrishnan 2004
  • Modeling and on-line recognition of PD signal buried in excessive noise Signal processing Shetty PK, Srikanth R, Ramu TS 2004; 84 (12): 2389-2401
  • Wavelet-based Coding of 2-D and 3-D coding of MR images IEEE-TENCON Srikanth Ryali, A.G. Ramakrishnan 2003
  • Parameter Estimation of HRF and Classification of fMRI data using Probabilistic PCA Modeling ICAPR Srikanth Ryali, A.G.Ramakrishnan 2003
  • MR Image Coding using Content-based Mesh and Context", International Symposium on Signal Processing and Applications Srikanth Ryali, A.G. Ramakrishnan 2003
  • Model based Bayesian approach for MR Image Segmentation ICAPR Niranjan Joshi, Srikanth Ryali, A.G.Ramakrishnan 2003
  • Parameter Estimation of HRF and Classification of fMRI data using Probabilistic PCA Modeling ICAPR Srikanth Ryali, A.G.Ramakrishnan 2003

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