CCECE 2014 1569880717
Brain Network Extraction From Probabilistic ICA Using Functional Magnetic Resonance Images and Advanced Template Matching Techniques Saman Sarraf 1,2, Cristina Saverino 1,3, Halleh Ghaderi 4, John Anderson 1,3 1 Rotman Research Institute at Baycrest, Toronto, Canada, 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada 3 Department of Psychology, University of Toronto, Toronto, Canada 4 Montreal Neurological Institute and Biomedical Engineering Department of McGill University, Montreal, Canada
[email protected]
Abstract – The human brain is a complicated network made-up of a large number of regions, which are structurally and/or functionally connected. Recently, neuroimaging studies using functional Magnetic Resonance Imaging have revealed that certain neural structures are highly active during periods of rest. Amongst several methods that have been developed to analyze resting-state fMRI data, Probabilistic Independent Component Analysis (PICA) is currently the most popular technique. The major challenge of using PICA is that resting-state networks are split into several components and visually extracting them can be difficult. In this paper, we propose a fast and precise algorithm based on advanced template matching in spatial domain such as Normalized Cross Correlation adapted to functional images in order to automatically extract the Default Mode Network (DMN) which is the task independent resting state network in the brain using PICA. We create a DMN template covering all reported regions in literature using two standard atlases. Ultimately, we reconstruct an image of the extracted DMN from PICA using an optimized decision making. Our approach was effective given that our algorithm results correlated highly with the DMN template.
known as resting state networks. Several methods have been developed to process functional MRI data, which can be categorized into two major groups: model-dependent, such as seed correlation analysis (SCA), and model-free methods, such as: principal component analysis (PCA), probabilistic independent component analysis (PICA), and graph-based method [2]. The most popular algorithm today is PICA [3]. The high level of consistency and reproducibility of PICA resulted in its wide use among neuroimaging researchers. Nevertheless, there is a major limitation that can often be found in the results of PICA. The resting-state networks are split into several components rather than being identified as a single component. This issue is the result of blind source separation algorithms that causes researchers to be incapable of easily distinguishing the resting-state networks from one another. Of even greater concern is that, the visual extraction of the resting-state networks from PICA components affects the consistency and reliability of the results, as each interpreter may identify different components to form the same network. Having an algorithm or pipeline that can “automatically” extract the resting-state networks from the PICA component is crucial for neuroimaging work. In this experiment, we propose an algorithm that can automatically extract one the most important resting-state networks, called the default mode network (DMN), from PICA components using a template matching technique in the spatial domain followed by an optimized decision making [4].
Keywords – fMRI; Resting-State; PICA; DMN template
I. INTRODUCTION Functional Magnetic Resonance Imaging (fMRI) is a technique that measures brain activity by detecting the associated changes in blood flow. This MRI technique uses the change in magnetization between oxygen-rich and oxygen-poor blood in the brain as its primary outcome measure, with greater consumption of oxygen corresponding to greater neural recruitment within the brain [1]. In the past three decades, a rich history of structural and functional neuroimaging studies have provided an enormous amount of knowledge about the organization and function of the human brain, especially regarding certain brain structures. Recently, advances in functional neuroimaging have also provided new tools to measure and examine functional interactions between brain regions, facilitating the examination of functional connectivity in the human brain [2]. In particular, functional connectivity analysis demonstrates that certain brain regions are highly coupled at rest and form a network of correlated structures,
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BACKGROUND AND ALGORITHMS
A. Functional Connectivity: Resting-State Brain Networks Our brain is an efficient network to be precise. It is a network made up of a large number of brain regions that have their own task and function but remain highly interactive by continuously sharing information with each other. As such, they form a complex integrative system in which information is continuously processed and transferred between structurally and functionally linked brain regions: the brain network. Exploring the human brain as an integrative network of functionally connected brain regions can provide new insights about large-
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CCECE 2014 Toronto, Canada
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instantaneous mixing process corrupted by additive Gaussian noise ߟሺݐሻǣ
scale neuronal communication in the human brain. It provides a platform to study how functional connectivity and information integration relates to human behavior and how this complex organization may be altered in neurodegenerative diseases. Functional connectivity is defined as the temporal dependency between spatially remote neurophysiological events. In the context of functional neuroimaging, functional connectivity is suggested to describe the relationship between the neuronal activation patterns of anatomically separated brain regions, reflecting the level of functional communication between regions. To summarize, resting state fMRI experiments are focused on mapping functional communication channels between brain regions by measuring the level of correlated dynamics of fMRI time-series [2]. Functional connectivity between brain regions likely plays a key role in complex cognitive processes, thriving on the continuous integration of information across different regions of the brain. This makes the examination of functional connectivity in the human brain very important and provides new insights about the core organization of the brain.
ݔ ൌ ݏܣ ߤ ߟ ሺͳሻ Where, A is signal, ݔ represents the -dimensional vector of individual measurements at voxel݅, ݏ is the ݍ-dimensional vector of non-Gaussian source signals ሺ ݍ൏ ሻ and finally, ߟ denotes Gaussian noise. The PICA was applied to the preprocessed data using FSL-MELODIC package [5] in order to obtain the PICA components. D. Advanced Template Matching Techniques The correlation between two signals (cross-correlation) is a standard approach to feature detection as well as a component of more sophisticated techniques. Here, the following equation represents a modified version of fast normalized crosscorrelation (NCC) [8] denoted byߛሺݑǡ ݒሻ which is compatible with fMRI data. ҧ ൧ሾݐሺ ݔെ ݑǡ ݕെ ݒሻ െ ݐҧሿ σ௫ǡ௬ൣ݂ሺݔǡ ݕሻ െ ݂௨ǡ௩ ሺʹሻ ଶ ҧ ሿ σ௫ǡ௬ሾݐሺ ݔെ ݑǡ ݕെ ݒሻ െ ݐҧሿଶ ሽǤହ ሼσ௫ǡ௬ሾ݂ሺݔǡ ݕሻ െ ݂௨ǡ௩
B. Data Aquistion and Pre-processing For this study, 7 males and 9 females with a mean age of 21.1 ± 2.2 years were recruited. All participants were healthy and had no reported history of medical or neurological disease. Scanning was performed on a Siemens Trio 3 Tesla MRI scanner. Anatomical scans were acquired with a 3D MP-RAGE sequence (TR=2s, TE=2.63 ms, FOV=25.6 cm, 256 x 256 matrix, 160 slices of 1mm thickness). Functional runs were obtained with an EPI sequence (150 volumes, TR=2 s, TE=30 ms, flip angle=70◦, FOV=20 cm, 64 x 64 matrix, 30 axial slices of 5mm thickness, no gap). The fMRI data have been pre-processed using LONI Pipeline Processing Environment (Version 5.9.1) [4]. The modules of this pipeline shown in Appendix Fig.1 have been developed using the functions of FMRIB Software Library v5.0 [5]. The pre-processing steps were applied to functional and anatomical data as following. The non-brain tissue from T1 anatomical images were deleted using Brain Extraction Tool [6]. The fMRI motion correction was applied using MCFLRIT [7]. Spatially smoothing of each functional data was applied using a Gaussian kernel of 5-mm full width at half maximum. Low level noise removal was performed by a high-pass temporal filtering with σ=90.0 seconds. Thereafter, the functional images was first aligned to the individual’s high resolution T1-weighted images which subsequently registered to the MNI152 standard space (average T1 brain image constructed from 152 normal subjects at Montreal Neurological Institute) using affine linear registration with 7 DOF (degrees of freedom) and 12 DOF for anatomical images and MNI standard space followed by 2mm resampling.
Where ݂ሺݔǡ ݕሻ is the original image and ݂ ҧ is the mean of image intensity in the region under the template, ݐis the template and ݐҧ represents the mean of image intensity in the template. In this study, the template has the same number of slice as the functional images. NCC is a fast and robust approach in template matching which can precisely define the fitted region to the template. Sum of Squared Differences (SSD) which is the most popular method in matching score is described by: ܵܵ ܦൌ
ሾ݂ሺݔǡ ݕሻ െ ݐሺ ݔെ ݑǡ ݕെ ݒሻሿଶ ሺ͵ሻ
௫ǡ௬
Where ݂ሺݔǡ ݕሻ describes the original image and ݐrepresents the image intensity in the template. We will see shortly, although SSD is a sensitive method to the zero-mean images, it can produce good results for this study. E. Default Mode Network Template Functional imaging studies have revealed that certain brain regions consistently show greater activity during resting-states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. Default mode network (DMN) plays an important role in cognitive processing and many researchers coming from different backgrounds such as neuroscience, psychology and biomedical engineering are interested in discovering more about this complicated and important brain network [9] [10]. DMN includes several brain regions such as Anterior Cingulate Gyrus (ACC), Posterior Cingulate Gyrus (PCC), Superior Lateral Occipital Cortex (LOC), Posterior Middle Temporal Gyrus (MTG), Parahippocampal Gyrus (PHG), Precuneus Cortex, and Superior Frontal Gyrus (SFG) [9] [10] [11]. The DMN template
C. Probabilistic Independent Component Analysis The probabilistic ICA (PICA) model [3] which is a special case of blind source separation is formulated as a generative linear latent variables model. It is characterized by assuming that the -variate vector of observations is generated from a set of ݍ statistically independent non-Gaussian sources via a linear
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shown in Fig.3. However, some small regions in the Temporal Gyrus have not been covered in the SSD results.
has been created in FSLVIEW [5] using Harvard-Oxford cortical and subcortical structural atlases [12]. This 3D template contained 91 slices of probabilistic brain maps (109x91), voxel size 2 mm shown in Appendix Fig.2. III.
RESULTS AND DISCUSSION
A. Experimental results PICA has been applied to the data and 84 components which were 3D brain maps (91x109x91) were extracted using FSLMELODIC [5]. As mentioned above, the resting-state networks were split into several components, which could not be visually distinguished. Our template matching algorithm, which was developed in MATLAB R2010b, automatically read and loaded the components into the memory. Then, a binary mask of each slice in the template as well as in the component was generated. Normalized Cross Correlation (Eq.2) and Sum of Squared Differences (Eq.3) were calculated for the corresponding slices one by one. For each component, the sum of NCCs and SSDs were also measured as a metric. Fig.1 and Fig.2 illustrate the Sum of all NCCs and SSDs across components. The increasing peaks in NCC and decreasing peaks in SSD represent the PICA components being highly matched with the DMN template.
Figure 3: Normalized NCC and SSD over PICA components
NCC and SSD were calculated for all 91 slices over 84 components in order to find the best slices in all data which were highly matched with the DMN template (Fig.4 and Fig.5). We observed the mean oscillations in the NCC values among PICA components. We could also see several peaks highly correlated to the DMN template which were sufficient to form our DMN in this dataset. SSD results in more uniform shape which has a considerable overlap with the NCC peaks.
Figure 1: Sum of NCCs of all Components over PICA Components Figure 4: NCC over Components and Slices
Figure 2: Sum of SSDs of all Components over PICA Components Figure 5: SSD over Components and Slices
As mentioned above, the NCC approach resulted in some sharp peaks which are potentially parts of DMN and showed a high correlation with the template. Although the SSD metric is recommended for block matching mostly in case of non-zero mean, it yielded the very similar results to the NCC method
B. Decision Making To create a final 3D image of DMN including the components having high NCC values, an optimized decision making model was developed based on the correlation coefficient definition and a priori knowledge of our DMN template. The software selected
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Differences, whereby differences were slightly better for numerical comparisons and moderately better in visual comparison, as validated by our expert.
the components whose NCC values were equal or greater than 0.7, because, statistically, this range is considered as high correlation. This threshold is adjustable by the users after looking at the NCC plots in order to have enough number of components forming the final DMN. In some cases, we need to decrease this threshold to have enough components to create a full image of the DMN due to low signal to noise ratio raw data. Furthermore, we considered a priori of the DMN template to select the appropriate components. We knew that there was no region of interest (ROI) in the first 13 slices as well as the last 10 slices in the DMN template therefore, if any component was selected in that range, it was ignored Fig.4 and Fig.5. The algorithm considered the lowest NCC value for the first 13 and last 10 slices. Finally, nine components were selected and three of them were in the range and were omitted. The final DMN image using NCC method was reconstructed by adding up the candidate components (Appendix Fig.3). The decision making model for the SSD method was slightly different to the previous one. In this step, we used a priori knowledge idea. We selected 10 components having the least SSD values and we took three of them out; therefore, the final image was created by seven components (Appendix Fig.4). Fig.6 compares the NCC values of three images over image slices: the whole data Vs. the DMN template (black curve), the image reconstructed from NCC method Vs. the DMN template (blue curve) and the image reconstructed from SSD method Vs. the DMN template (red curve). At first glance, we perceive that the NCC values have significantly increased (around 3-folds) using the reconstructed images. In several slices, the NCC values of two reconstructed images are highly similar, showing a high and consistence results in both methods. Furthermore, the NCC approach slightly shows a better performance than the SSD one.
ACKNOWLEDGMENT We would like to express our gratitude towards Dr. Cheryl Grady, senior scientist of Rotman Research Institute and professor at Dept. of Psychology and Psychiatry, University of Toronto, for extending her help and support in this study. REFERENCES [1] Scott A. Huettel, Allen W. Song, Gregory McCarthy, Functional Magnetic Resonance Imaging, Sunderland, Massachusetts: Sinauer Associates, December 2008. [2] Martijn P. van den Heuvel, Hilleke E. Hulshoff Pol, "Exploring the brain network: A review on resting-state fMRI functional connectivity," European Neuropsychopharmacology, vol. 20, no. 23 March 2010, p. 519–534, 2010. [3] Beckmann C.F. , Smith S.M., "Probabilistic independent component analysis for functional magnetic resonance imaging," IEEE Transactions on Medical Imaging, vol. 23, no. 2, pp. 137 - 152, 2004. [4] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing (3rd Edition), Prentice Hall, 2008. [5] Federica Torri, Ivo D. Dinov, Alen Zamanyan, Sam Hobel, Alex Genco, Petros Petrosyan, Andrew P. Clark, Zhizhong Liu, Paul Eggert, Jonathan Pierce, James A. Knowles, Joseph Ames, Carl Kesselman, Arthur W. Toga, Steven G. Potkin, Marquis P. Vawter, Fabio Ma, "Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows," Genes, vol. 3, no. 30 August 2012, pp. 545-575, 2012. [6] S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews, "Advances in functional and structural MR image analysis and implementation as FSL," NeuroImage, , vol. 23(S1), pp. 208-19, 2004. [7] S. Smith., "Fast robust automated brain extraction," Human Brain Mapping, vol. 17(3), no. November 2002, pp. 143-155, 2002. [8] Jenkinson, M., Bannister, P., Brady, J. M. and Smith, S. M. , "Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images," NeuroImage, vol. 17(2), pp. 825-841, 2002. [9] J. P. Lewis, "Fast Template Matching," Vision Interface, pp. 120-123, 1995. [10] Michael D. Greicius, Ben Krasnow, Allan L. Reiss, and Vinod Menon, "Functional connectivity in the resting brain: A network analysis of the default mode hypothesis," NEUROSCIENCE, vol. 100, p. 253– 258, 2003.
Figure 6: NCC comparison between Data and Reconstructed Images from NCC and SSD methods
[11] Michael D. Fox, Abraham Z. Snyder, Justin L. Vincent, Maurizio Corbetta, David C. Van Essen, and Marcus E. Raichle, "The human brain is intrinsically organized into dynamic, anticorrelated functional networks," NEUROSCIENCE, vol. 102 , p. 9673–9678, 2005.
CONCLUSION In this paper, we proposed a fast two-step algorithm in order to automatically extract DMN in fMRI resting-state networks from PICA components using a spatial template matching technique followed by a decision making step. This experiment was performed on a large number of subjects. PICA components demonstrated that Normalized Cross Correlation procedures resulted in better template matching than Sum of Squared
[12] Roberto Toro, Peter T. Fox and Tomas Paus, "Functional Coactivation Map of the Human Brain," Cerebral Cortex , vol. 18, pp. 2553--2559, 2008. [13] Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone SV, "Decreased volume of left and total anterior insular lobule in schizophrenia.," Schizophr Res., Vols. 83, pp. 155-71, 2006.
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APPENDIX
Fig.1 Pre-processing pipeline implemented in LONI environment using FMRIB library
Fig.2: Default Mode Network template generated using Harvard-Oxford cortical and subcortical structural atlases
Fig.3: Default Mode Network extracted from PICA components using Normalized Cross Correlation approach
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Fig.4: Default Mode Network extracted from PICA components Sum of Squared Differences using approach
Fig.5: Left: Coronal, middle: Sagittal, right: Axial view of the brain Anterior Cingulate Gyrus (ACC) Top: DMN template created using Harvard-Oxford atlases Middle: DMN reconstructed from NCC approach Bottom: DMN reconstructed from SSD approach
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