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Vasileios Megalooikonomou

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?.

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Vasileios Megalooikonomou

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  1. Mining Structure-Function Associations in a Brain Image Database Vasileios Megalooikonomou Department of Computer Science Dartmouth College

  2. BRAID: Brain-Image Database Nick Bryan Christos Davatzikos Joan Gerring Edward Herskovits Vasileios Megalooikonomou

  3. 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)

  4. Overview • Goals • Background • Methods • Results • Discussion - Future Work

  5. Goals • Structure-function correlation • Decoupling of signal and morphology • Scalability (large longitudinal studies) • Transparent management of diverse data sources

  6. 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)

  7. Background: Spatial Normalization of Brain Images Before Spatial Normalization After Spatial Normalization

  8. Background: Spatial Normalization: Example • 3D elastically deformable model (Davatzikos, 1997) original target deformed Deform MRI to Talairach atlas

  9. Background: Talairach Atlas

  10. Background: Gyri Atlas

  11. 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 ;

  12. Methods • Segmentation • Registration • Integration into BRAID • Visualization • Statistical analysis

  13. BRAID: Flow of Information MRI Registered Lesions Atlas Clinical Data Image Segmentation Structure-Function Association Analysis Image Registration Lesions

  14. 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

  15. 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 ;

  16. 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

  17. 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

  18. Methods: Statistical: Chi-square • 2 x 2 contingency tables for categorical variables • Pearson chi-square

  19. Methods: Statistical: Voxel-based: Logistic Regression where Identify “causal brain region” that best discriminates affected/unaffected subjects 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

  20. Structural Fisher’s Exact Mann-Whitney Variable p-value 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: FLIC study- ADHD

  21. Results: Atlas based: CHS study Chi-square p-value S-Bonf. Correct. p-value 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 0.00001 0.00001 0.00002 0.00003 0.00003 0.00003 0.0039 0.0095 0.0195 0.0224 0.0224 0.0224

  22. Results: Voxel-based: FLIC study

  23. Results: Voxel based: 3D reconstruction: FLIC study

  24. Results: Voxel-based Regression Analysis ADHD+ ADHD- Optimal_Regression_Sphere

  25. 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

  26. 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

  27. 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 F • For each subject p instantiate S-nodes to produce F p

  28. Results: Simulator

  29. Results: Simulator

  30. Results: Simulator

  31. 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

  32. 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

  33. Analysis, Classification and Visualization of Probabilistic 3D Objects

  34. 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.

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