A. Specific Aims Unchanged

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1 A. Specific Aims Unchanged B. Studies and Results The goal of this project is to develop computational methods for processing and analysis of high angular resolution diffusion imaging data that has been fitted with higher order diffusion models (HOMs) and apply these methods to study autism spectrum disorders (ASD). The diagnosis of autism spectrum disorder (ASD) is currently based on studying the behavior and developmental history of the child. With the development of advanced forms of diffusion-weighted magnetic resonance imaging (DW-MRI), it is expected that imaging will elucidate pathology-induced and neuro-developmental changes in white matter (WM) architecture, and provide diagnostic and predictive anatomical biomarkers. Summary of the progress made by the team in the current reporting period of 8 months (October 1, 2010 June 1, 2011) can be found below described under the originally funded specific aims. 1. The first aim was focused on HOMs measures and spatial normalization. Specifically 1) to develop scalar diffusion measures and connectivity maps from HOMs; and 2) to use these measures and maps to develop and validate a feature-based deformable registration algorithm for HOM images preparing them for subsequent analysis. Here are the major achievements: a. HOM-based scalar measures and connectivity maps: i. Peak-based measures: We have introduced the primary peak vector of the FOD as a clinically meaningful measure, analogous to the principal eigenvector of DTI. The analysis of the peak amplitude and orientation is expected to provide comprehensive information about the proportion and direction of fibers passing through a voxel. The FOD model is particularly amenable to the calculation of peaks due to the sharpness of the lobes of the FOD model; however the method can be applied to ODF models with sharpening before peak computation, making the peak-derived measures generalizable. The image on the right shows a peak-based map. ii. Finsler connectivity maps: We have developed an efficient method for estimating a global measure for HOM. A parallel implementation of the Finsler tractography method was implemented. This method calculates geodesic paths in the diffusion data, defining connectivity between regions in the brain. In this methodology, the Finsler distance from a set of seeding points to each other point in the imaged volume is computed, so that the fiber bundles can be traced from a set of target points back to the seeds following minimum distance paths. The local cost of arriving to the current position from each neighboring voxel is computed as a function of the HOM derived from the HARDI diffusion data. The global minimization of the cost function is carried out via the Fast Sweeping algorithm, which recursively minimizes the local costs of each voxel based its neighbors. The implementation of the method is done in the open source insight toolkit (ITK). For each possible sweeping direction, and each voxel the directional cost of reaching the voxel from each neighbor is added, while keeping the minimum cost of reaching the current voxel. The team has also integrated the Finsler tractography method into the open source platform 3DSlicer. A screenshot can be seen in the adjoining figure. The resulting Finsler connectivity map is color coded, near to far, yellow-green-blue-red. This map is a global measure derived from the HOMs and defines connectivity between brain regions.

2 iii. Random walk based connectivity density maps: A novel discrete stochastic tractography method was developed. While modeling a particle random walk process similar to existing methods, our method is designed to examine the steady state behavior of a large number of particles with that aim of increasing the robustness when tracking from small cortical regions. The figure on the right shows a nodal density map generated from using this tractography. These maps can be used for population statistics as well as to aid registration. b. Spatial normalization: i. A two stage non-linear diffeomorphic demons registration algorithm was developed to perform spatial normalization between two FOD images. The algorithm was validated on 10 simulated FOD datasets as well as 10 in-vivo (b=1000s/mm 2 ) FOD datasets, showing a comparable accuracy to the orientation sensitive method but in less time, while out-performing the orientation invariant method as well as standard affine and deformable T2 registration methods. The adjoining figure shows the reduction in variance in the dataset from affine registration (A) to FOD-based registration (B) (red indicates high variance and blue depicts low variance with yellow lying in between). ii. We have developed a novel method for spatial alignment of white matter tract bundles traced from diffusion images [4]. The method is based on a Gaussian process (GP) representation of tract density maps (TDM). Such a representation avoids the need for point-to-point correspondences between tracts, and is robust to tract interruptions. Another advantage of this method being a parametric model, the approach does not require spatial resampling of the fiber bundles during the alignment process. The method produces a dense deformation field describing correspondence of the white matter bundles. The figure compares the results from a traditional volumetric registration method (a) with the results obtained with the novel method (b). Note the better alignment between the red and blue fiber bundles with the new method. 2. The second aim was focused on developing and validating an integrated framework for population statistics on HOMs measures. Here are the major achievements: a. Clustering: An algorithm for the division of neuronal white matter into regions of homogeneous WM architecture as represented by the FOD diffusion model was developed. From an FOD image and a mask identifying the WM volume, the algorithm computes the similarity between every pair of WM voxels. This similarity matrix is then iteratively partitioned using the normalized cuts spectral cluster algorithm in combination with a routine to ensure the spatial connectedness of each cluster. The outcome of this process is a collection of spatially compact regions each having a low FOD variance. This low variance is critical as it implies that the mean FOD of these regions can be extracted and will reliably reflect the WM architecture of the underlying WM. See figure below for the data-driven clustering. (Details in [5])

3 b. Atlas creation: The spatial normalization and clustering are being combined to generate an atlas from normalized FOD images of healthy young adults and adolescents. The non-linear registration reduces the population variance when compared with affine registration that is typically used in determining population based atlases. The automated clustering routine is very useful in the absence of intensive manual delineation of anatomical regions by a neuroanatomist, while generating more coherent regions that are more applicable for further statistical study. The figure on the right shows the comparison between the anatomybased and data-driven atlas, with the latter demonstrating reduced ROI-wise variance. 3. The third aim was focused on designing and validating multivariate Pattern Classifiers of HOM features and obtaining an abnormality score for each subject. Here are the major achievements: a. HARDI classifiers: An FOD atlas was first generated using the methods described in 2b for 22 TD subjects. The average real spherical harmonic (RSH) spectral power was extracted from each region defined in the atlas which were then used in principle component analysis (PCA) and used to train a linear support vector machine classifier (L-SVM). Fivefold cross validation was used to investigate the classification framework. (Details in [6]) b. MEG classifiers: Language impairment (LI) is an important behavioral symptom associated with autism. Two measures of auditory processing revealed by MEG that may relate to LI in ASD are the latency of auditory evoked neuromagnetic field 100 ms component (M100) (which is a response component reflecting detection of auditory input) and latency of the mismatch field (MMF) (which is a response component reflecting detection of change in the auditory stream). While it has been demonstrated by Dr. Roberts group that M100 could be used to distinguish between autistic and typically developing (TD) subjects, this measurement, however, could not distinguish between language-impaired (LI+) and non-language-impaired (LI-) subjects. The MMF latency, a measure of how quickly the brain detects changes among sounds or phonemes, is lengthened in ASD and may also be a measure of LI (Roberts et al., 2008, 2011). While these univariate measures characterize some aspect of LI/ASD, we hypothesized that their appropriate combination will improve the group distinction. We have therefore developed two-way LDA-based and three-way SVM based classifiers combining the M100 and MMF measures for each subject as features. (Details in abstract [2], journal paper under preparation). c. MEG-DTI classifiers: As it has been shown by Dr. Roberts group that diffusion measures contribute to the understanding of LI, we extended the MEG classifiers to include diffusion information. We chose only those ROI s that were hypothesized to be affected in ASD/LI. These included (left and right): Superior temporal white matter (STWM), Superior longitudinal fasciculi (SLF), Inferior fronto-occipital fasciculi (IFOF) and Inferior longitudinal fasciculi (ILF). We then skeletonized these fiber tracts using FSL-TBSS [4] and used the FA values on this skeleton as our DTI features. These were combined with the two MEG measures to create cross-validated 3-way SVM classifiers. (Details in [3]) 4. The fourth aim focused on the application of the methods developed to the data collected. The following was the progress made. a. Data acquisition i. During the project period, Dr Roberts has supervised the successful acquisition and scanner quality control for 113 HARDI acquisitions in children with ASD and typical development

4 Program Director/Principal Investigator (Last, First, Middle):Verma, Ragini (TD) under the protocols PI d by Dr Schultz and himself. All images are acquired with bvalue of 3000s/mm2 and 64 directions. As a part of this procedure, an eddy current heating problem which manifested as an emergence of fat signal (artifact) after about 40 gradient directions (arising from a progressive frequency drift) was swiftly identified and has been fixed by devising a protocol where two sets of HARDI images (33 and 31 directions respectively) are acquired with a center frequency reset between them, allowing successful spectrally-based fat suppression over the entire acquisition. (Enrollment table attached) ii. Additionally the Stejskal-Tanner monopolar diffusion gradient pulse sequence has been replaced with a similar Works-in-Progress sequence, obtained in collaboration between Dr Roberts and Siemens Medical Solutions (advdiff_511c), and allowing direct reporting of not just the b-value, but importantly the inter-gradient separation ( big delta, Δ). This allows interpretation of differences between DTI (b=1000 s/mm2 and Δ = 35ms) and HARDI (b=3000 s/mm2 and Δ = 53ms) acquisitions, as well as paves the way for independent manipulation of b-value and Δ, for studies of white matter microstructure (using approaches akin to AxCaliber) b. Population Studies: i. Comparison with DTI: We studied the differences in fiber tracking of the Arcuate Fasciculus (AF) in DTI and HARDI data in 21 TD and 29 ASD subjects. As seen in the figure, Arcuate Fasciculus can be localized significantly better (closer to the anatomical definition of the tract) using HARDI due to a better characterization of complex white matter(relative to DTI) in patient and control populations. This establishes the importance and feasibility of HARDI acquisitions and tracking in children and in clinical populations. Details can be found in [1]. ii. HARDI pattern classifiers (described in 3a) were trained on an ASD imaging dataset (ASD=23/TDC=22). Fivefold cross validation gave 78% accuracy, 78% sensitivity, and 77% specificity. An examination of the weights used by the classifier to generate each score, showed approximately level contributions from the RSH orders 0, 2 and 4, suggesting that higher angular frequency information available in orders 6 and 8 was either highly variable across the population or inherently less reliable due to noise. The regional contributions to the classification score showed (in the adjoining figure) large contributions from portions of internal capsule as well as from the splenium of the corpus callosum, regions that have been previously implicated in ASD. (Details in [6]). Region based DTI classifiers were created for an ASD population. The ROIs were picked from a diffusion atlas. Details can be found in [7]. iii. Pattern classifiers developed on MEG measures: We used M100 and MMF latencies in 52 children with ASD (15 LI+ and 37 LI-) and 21 age-matched TDC to create MEG-based classifiers described in 3b above. Two way SVM showed 85.5% accuracy in separating ASD from TD (see figure) and the three way SVM showed 71.4% accuracy in separating LI+, LI- and TDC. (Details in [2]) iv. Pattern classifiers combining DTI-MEG measures: We used 9 ASD/LI+, 23 ASD/LI-, 21 TD children to train DTI-MEG classifiers PHS 398/2590 (Rev. 06/09) Page Continuation Format Page

5 discussed in 3c. The average LOO accuracy was 71.69% (34/53) correctly classified. The top 8 features were used in each LOO. The top feature was MMF latency (rank 1), while M100 was selected frequently at rank 5, with diffusion features selected in the intermediate ranks. The adjoining figure shows the plotted abnormality scores produced by the classifier, showing group separation. (Details in [3]) Summary: The Penn-CHOP team has contributed towards the development of global measures for HOMs, spatial normalization technique, clustering and pattern classification and has started applying the methods to the data that has been currently acquired. The CHOP team consisting of Drs. Roberts and Schultz have led the acquisition and provided over 100 datasets. Dr. Roberts has generated MEG derived measures for Aim 4, that are currently being used in classification. The BWH-Harvard team has focused on the Design of connectivity measures for HOMs (Aim 1) and Spatial normalization for white matter (Aim 1). Collaboration: Drs. Verma and Westin have monthly phone calls to discuss the progress of the project. Antonio Tristan-Vega from the BWH-Harvard site will spend a week at UPenn in June 2011 to install the Finsler tractography software, and to get familiar with the Autism data acquired at UPenn. Drs. Verma and Roberts also have monthly meetings on data acquisition and MEG-HARDI combination via pattern classifiers. Revisions (formerly Supplements): N/A C. Significance The work in the project is highly significant. Correct assessment of WM architecture helps elucidate relationship or structural connectivity between different brain regions, a rapidly emerging area of research. Disruptions in these connectivities are hypothesized to subserve deficits observed in autism such as impaired social interactions, impaired language and communication and stereotypical, restricted and repetitive behaviors. While the spatial normalization algorithms enable population studies, the scalar measures being computed from the HOMs provide clinically meaningful measures on which the population studies can be conducted. The tractography methods are expected to provide connectivity measures that describe the underlying pathology as well as seed the creation of connectivity matrices that can serve as features for classifiers. Classifiers generate an abnormality score quantifying the underlying pathology as well as provide a ranking of regions that contributed to the group difference. These imaging measures and classifier-based abnormality scores when correlated with clinical measures of symptom severity will provide additional insight into the pathology and its progression, thus making the developed methods clinically significant. D. Plans There are no significant changes to the current research plan. We plan to continue as outlined in the original proposal. In particular: 1. Aim 1: Connectivity measures: We will develop connectivity measures from the two tractography algorithms mentioned above (1b,c). In particular, the random-walk tractography is being developed into a connectivity measure. Nodal connection density will be computed by examining the equilibrium behavior of the conditional probability matrix which, under the basic assumption used in Monte-Carlo fiber tracking, is proportional to fiber density connected to that region. This density is expected to provide a scalar field that can be mapped to the WM volume surface and, depending on its spatial scale, may prove useful for either GM parcellation or for WM subject to subject registration. The nodal connection density will then be combined with the conditional probability matrix yielding the probabilistic connection density matrix, which can be used in group statistical studies or to investigate the topology of the network. We are also developing geo-variance measures for HOMs. Spatial normalization: Work in [4] will be adapted to HARDI Antonio Tristan-Vega from the BWH-Harvard site will spend a week at UPENN in June 2011 to install the Finsler tractography software, and to get familiar with the Autism data acquired at UPenn.

6 Creating a geometric variance measure for HOMs. Create cluster based features that can be used for classification. 2. Aim 2: The data-based atlas created in 2c does not require a trained neuroanatomist and hence will be extremely useful as it can be generated for young brains for which there are no atlases and templates available. However, recognizing the fact that hypotheses driven studies will gain from anatomical context, we are setting up an automated correspondence with the DTI-based Eve atlas of Susumu Mori. This will be corrected by Dr. Schultz s group and hence lead to a child anatomical and data driven HARDI atlas for children. The Finsler maps will be used for population studies 3. Aim 3 The MEG measures will be combined with the HARDI measures to create joint classifiers. Connectivity-based features will be used to generate HARDI classifiers 4. Aim 4: We plan to continue recruitment of children with ASD and TD. We plan to commence studies in health adults, wherein b-value and the number of encoding directions will be systematically varied in the range s/mm 2 and , respectively. We expect to complete postdoctoral fellow recruitment and hiring. Currently two people are being interviewed. This postdoc will help develop pulse sequence approach to independently vary b-value and Δ, such that 4 acquisitions (high/low b-value, short/long Δ) can be obtained; develop analysis approach to infer mean free path of diffusion under these differing conditions and infer a mean or effective restriction distance. MEG based measures will be compared with Finsler maps. 5. Further work will focus on the development of discriminative statistics and correlation analyses to enable comparing. Discriminative statistics are fundamental when studying pathologies and comparing populations Publications: We have 3 abstracts, 3 peer-reviewed conferences and 1 journal publication. Journal publications are being prepared by extending the abstracts and conference papers, as well as describing the classifiers (MEG, HARDI), atlas creation and connectivity analysis. In summary, there has been no change in our future plans. There was a slow start in the project due to the delay in getting postdocs with specialized abilities like diffusion pulse programming and HARDI analysis, but currently two postdocs are being interviewed and we expect to fill the acquisition postdoc person by July 1, We expect the project to continue to be highly productive in the upcoming year. Human subjects: There is no change in this regard from the original submission. The human subjects data is being acquired under the supervision of Dr. Roberts and Dr. Schultz as outlined in the grant. Publications in the project 1. H. Eavani, L. Bloy, J. Herrington, R. T. Schultz and R. Verma, Fiber tracking of the Arcuate Fasciculus in Autism using High Angular Resolution Diffusion Imaging, ISMRM William A. Parker, Madhura Ingalhalikar, Ragini Verma and Timothy P.L. Roberts, Multivariate MEG Pattern Classifiers for Language Impairment In Autism, IMFAR Madhura Ingalhalikar, Drew Parker, Timothy P.L. Roberts, and Ragini Verma, Diagnostic prediction of language impairment in Autism Spectrum Disorder using joint MEG - DTI classification, ISMRM D. Wassermann, Y. Rathi, S. Bouix, M. Kubicki, R. Kikinis, M. E. Shenton, C-F Westin, White Matter Bundle Registration and Population Analysis Based on Gaussian Processes, Proc. Of Information Processing and Medical Imaging (IPMI 11), 2011.

7 5. L. Bloy, M. Ingalhalikar, R. Verma, Neuronal White Matter Parcellation Using Spatially Coherent Normalized Cuts, Proc. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, L. Bloy, M. Ingalhalikar, H. Eavani, T. Roberts, R. Schultz, R. Verma, HARDI based Pattern Classifiers for the Identification of White Matter Pathologies, Medical Image Computing and Computer Assisted Intervention, M. Ingalhalikar, D. Parker, L. Bloy, T. Roberts, R. Verma, Diffusion based Abnormality Markers of Pathology: Towards Learned Diagnostic Prediction of ASD, Neuroimage, 2011, in press.