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Mining Structure-Function Associations in a Brain Image Database Vasileios Megalooikonomou Department of Computer Science Dartmouth College BRAID: Brain-Image Database Nick Bryan Christos Davatzikos Joan Gerring Edward Herskovits Vasileios Megalooikonomou What is data mining? • Now that we have gathered so much data, what do we do with it? • Extract interesting patterns (automatically) • Associations (e.g., butter + bread --> milk) • Sequences (e.g., temporal data related to stock market) • Rules that partition the data (e.g., store location problem) • What patterns are “interesting”? information content, confidence and support, unexpectedness, actionability (utility in decision making) Overview • • • • • Goals Background Methods Results Discussion - Future Work Goals • • • • Structure-function correlation Decoupling of signal and morphology Scalability (large longitudinal studies) Transparent management of diverse data sources Background • • • • • Illustra Object-Relational DBMS Image datablade Web interface Lesions identified manually Images registered to a common spatial standard (Talairach atlas) • Clinical information and images are integrated • Clinical studies (CHS, FLIC, BLSA) Background: Spatial Normalization of Brain Images Before Spatial Normalization After Spatial Normalization Background: Spatial Normalization: Example • 3D elastically deformable model (Davatzikos, 1997) original Deform MRI to Talairach atlas target deformed Background: Talairach Atlas Background: Gyri Atlas Background: Sample SQL queries • • • COMPUTE VOLUME OF A GIVEN STRUCTURE return volume((select unique image from structures where side='Left' and atlas='Brodmann' and name='17')) ; • • • • • DISPLAY GIF OF ALL LESIONS SUMMED UP insert into temp_image_1 values(permanent(map_image(sum_images(( select image from patient_images where image.description='All Lesions')), 'redgreenscale'))) ; select TS.SliceNo, slice(TS.SliceNo,overlay.image)::GIF as LesionDensity from TalairachSlices TS, temp_image_1 overlay order by SliceNo ; Methods • • • • • Segmentation Registration Integration into BRAID Visualization Statistical analysis BRAID: Flow of Information MRI Registered Lesions Atlas Clinical Data Image Segmentation Lesions Image Registration Structure-Function Association Analysis Methods: Visualization: FLIC study Sum of lesions for the ADHD- and ADHD+ groups ADHD(n=61) ADHD+ (n=15) Tal-107 Tal-113 Tal-116 Tal-119 Tal-124 SQL query: Sum of lesions for ADHD subjects • • • • • • • • • • • • • • insert into temp_image_1 values(permanent( map_image(sum_images((select image from patient_images where image.description='All Lesions' and patient in (select patient from attributes where varname='ADHD_GRP' and real_value=2 and patient like 'FLIC%'))), 'redgreenscale') + map_image((select unique image from structures where side='Left' and atlas='Talairach' and name='cortex') + (select unique image from structures where side='Right' and atlas='Talairach' and name='cortex'), 'bluescale') + map_image((select unique image from structures where side='Right' and atlas='Talairach' and name='putamen'), 'redscale') + map_image((select unique image from structures where side='Left' and atlas='CHS' and name='thalamus'), 'greenscale'))); select TS.SliceNo, slice(TS.SliceNo,overlay.image)::GIF as LesionDensity from TalairachSlices TS, temp_image_1 overlay order by SliceNo ; Methods: Statistical Analysis •Atlas based •Map each lesion onto at least one atlas structure •Prior knowledge increases the sensitivity of spatial analysis •Marked data reduction: 107 voxels 102 structures •Structural variables: categorical or continuous •Atlas free (voxel-based) •No model on the image data •Cluster voxels by functional association Methods: Statistical: Atlas Based • F functional variables, S anatomical structures • Analysis • Categorical structural variables • Exploratory • F x S contingency tables, Chi-square/Fisher exact test • multiple comparison problem • log-linear analysis, multivariate Bayesian • Directed using visualization, prior knowledge • small number of hypotheses to test • no multiple comparison problem • Continuous structural variables • Logistic regression, Mann-Whitney Methods: Statistical: Chi-square • 2 x 2 contingency tables for categorical variables • Pearson chi-square Methods: Statistical: Voxel-based: Logistic Regression • logit d log oddsd 1 x1 k xk where oddsd pd . 1 pd . • Identify “causal brain region” that best discriminates affected/unaffected subjects • logit d af b where • f = volume(intersect(Lesion, Sphere)) / volume(Sphere) • d = deficit (e.g., hemiparesis) • a = log odds / lesioned fraction of sphere volume • b = prior log odds of d • Optimize sphere parameters x, y, z, r Results: Atlas based: FLIC study- ADHD Structural Variable Fisher’s Exact p-value Mann-Whitney p-value Right Putamen 0.065 0.033 Left Thalamus 0.095 0.093 Right Caudate 0.168 0.115 Left Putamen 0.670 0.824 Results: Atlas based: CHS study Structure Function R globus pallidus L hippocampus R gyri angular R gyri orbital R gyri cuneus R optic tract R hemiparesis R visual defect L pronator drift L visual defect L visual defect L pronator drift Chi-square S-Bonf. Correct. p-value p-value 0.00001 0.00001 0.00002 0.00003 0.00003 0.00003 0.0039 0.0095 0.0195 0.0224 0.0224 0.0224 Results: Voxel-based: FLIC study Results: Voxel based: 3D reconstruction: FLIC study Results: Voxel-based Regression Analysis ADHD+ ADHD- Optimal_Regression_Sphere Methods: Validation •Objective: to evaluate BRAID’s analytical capabilities •Problems: not enough subjects, true assocs unknown, registration error •Approach: •Lesion-Deficit Simulator (LDS) + Monte Carlo analysis •measure effect of strength of assocs, model complexity, registration error, statistical power of tests •Application: a test-bed for development and evaluation of S-F correlation methods Validation: Background • Bayesian Network Model for S-F associations • Consider 3 cases for cond. prob. table, noisy-OR model case description deficit cond. probs. struct1 struct2 p(func=normal) 1 2 3 strong moderate weak 0/1 0.25 / 0.75 0.49 / 0.51 N N A A N A N A 0.75 0.25 0.25 0.06 Validation: Lesion-Deficit Simulator (LDS) • For each subject p • produce lesions: • obtain params for lesion size, number, spatial distr. • construct pdfs • produce simulated lesions given the pdfs • model registration error • estimate 3D Gaussian using landmarks • produce displacements of lesion centroids • find lesioned structures and priors of abnormality • use fraction of lesioned volume and threshold S • Sample priors for abnormality of structures and produce S p • Generate BN model of assocs among S-F • For each subject p instantiate S-nodes to produce F Fp Results: Simulator Results: Simulator Results: Simulator Results: Simulator • N is inversely proportional to the smallest prior/conditional probability • The degree of assocs affects more the performance than the number of assocs • On average 87% of assocs were found in registered images compared with perfect registration Discussion - Future Work • • • • • • neural-network and other non-statistical models bayesian multivariate analysis more complex spatial models increase number of subjects in BRAID automate methods for image segmentation statistical analysis of morphological variability Analysis, Classification and Visualization of Probabilistic 3D Objects For more information... • www.cs.dartmouth.edu/~vasilis, braid.rad.jhu.edu • V. Megalooikonomou, C. Davatzikos, E. Herskovits, “Mining Lesion-Deficit Associations in a Brain Image Database”, ACM SIGKDD, Aug. 1999, San Diego, CA, pp. 347-351. • V. Megalooikonomou, C. Davatzikos, E. Herskovits, “A Simulator for Evaluation of Methods for the Detection of Lesion-Deficit Associations”, Human Brain Mapping, in press. • V. Megalooikonomou and E. Herskovits, “Mining Structure-Function Associations in a Brain Image Database”, chapter in Medical Data Mining and Knowledge Discovery, K. J. Cios (ed.), Springer-Verlag, to appear in 2000. • V. Megalooikonomou, J. Ford, L. Shen, F. Makedon, “Data Mining in Brain Imaging”, Statistical Methods in Medical Research, to appear (invited paper). • E. H. Herskovits, V. Megalooikonomou, C. Davatzikos, A. Chen, R. N. Bryan, J. Gerring, “Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit hyperactivity disorder? Analysis with brain image database”, Radiology, Vol. 213, No. 2, pp. 389-394, 1999.