Vasileios Megalooikonomou

Department of Computer Science

Dartmouth College

Mining Structure-Function Associations in a Brain Image Database

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)

BeforeSpatialNormalization

AfterSpatialNormalization

Background: Spatial Normalization of Brain Images

Background: Spatial Normalization: Example

• 3D elastically deformable model (Davatzikos, 1997)

deformedoriginal target

Deform MRIto Talairach atlas

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

ImageSegmentation

LesionsImage

Registration

AtlasRegisteredLesions

ClinicalData

Structure-FunctionAssociation Analysis

Methods: Visualization: FLIC study

ADHD-

ADHD+

Tal-113 Tal-116 Tal-119 Tal-124Tal-107

Sum of lesions for the ADHD- and ADHD+ groups

(n=61)

(n=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 ;

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

•Structural variables: categorical or continuous

•Atlas free (voxel-based)•No model on the image data

•Cluster voxels by functional association

102 structures

Methods: Statistical: Atlas Based• F functional variables, S anatomical structures

• Analysis• Categorical structural variables

• Continuous structural variables

• Exploratory

• Directed using visualization, prior knowledge

• F x S contingency tables, Chi-square/Fisher exact test• multiple comparison problem• log-linear analysis, multivariate Bayesian

• small number of hypotheses to test• no multiple comparison problem

• 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

• kkd xxodds 11logdlogit

•

.1

.

d

dd p

podds

where

• baf dlogit where

Identify “causal brain region” that best discriminates affected/unaffectedsubjects

• 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 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: CHS study

Structure FunctionChi-square p-value

S-Bonf. Correct. p-value

R globus pallidusL hippocampusR gyri angularR gyri orbitalR gyri cuneusR optic tract

R hemiparesisR visual defectL pronator driftL visual defectL visual defectL pronator drift

0.000010.000010.000020.000030.000030.00003

0.00390.00950.01950.02240.02240.0224

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

struct1 struct2 p(func=normal)NNAA

NANA

0.750.250.250.06

case description deficit cond. probs. 123

strongmoderateweak

0 / 10.25 / 0.750.49 / 0.51

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

• Sample priors for abnormality of structures and produce S

• Generate BN model of assocs among S-F

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

p

pF

S

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.