Mining Brain Region Connectivity for Mining Brain Region Connectivity for Alzheimer's Disease Study via Sparse Alzheimer's Disease Study via Sparse

Inverse Covariance EstimationInverse Covariance Estimation

Jieping Ye

Arizona State University

Team Members

• Arizona State University– Jieping Ye (CSE)– Liang Sun (CSE)– Jun Liu (CSE)– Teresa Wu (IE)– Jing Li (IE)– Rinkal Patel (CSE)

• Banner Alzheimer’s Institute and Banner PET Center– Kewei Chen– Eric Reiman

Alzheimer’s Disease (AD)

• Currently, approximately 5 million people in the US – about 10% of the population over 60 are afflicted by Alzheimer’s disease (AD).

• The direct cost to care the patients by family members or health care professional is estimated to be over $100 billion per year.

• As the population ages over the next several decades, it is expected that the AD cases and the associated costs will go up dramatically.

Effective diagnosis of Alzheimer’s disease (AD) is of primary importance in biomedical research.

Neuroimaging: MRI

MRI is a high-resolution structural imaging technique that allows for the visualization of brain anatomy with a high degree of contrast between brain tissue types.

Neuroimaging parameters are sensitive and consistent measures of AD.

Reduced gray matter volume (colored areas) detected by MRI voxel-based morphometry in AD patients compared to normal healthy controls.

Neuroimaging: PET

FDG-PET: [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography is a functional imaging technique that measures the cerebral metabolic rate for glucose.

Neuroimaging parameters are sensitive and consistent measures of AD.

FDG-PET

AD Patient Versus Normal Control

Normal Control AD Patient

Connectivity Study for AD

• Recent studies have demonstrated that AD is closely related to the alternations of the brain network, i.e., the connectivity among different brain regions– AD patients have decreased hippocampus connectivity with

prefrontal cortex (Grady et al. 2001) and cingulate cortex (Heun et al. 2006).

• Brain regions are moderately or less inter-connected for AD patients, and cognitive decline in AD patients is associated with disrupted functional connectivity in the brain – Celone et al. 2006, Rombouts et al. 2005, Lustig et al. 2006.

Our Hypothesis

• There is significant, quantifiable difference in brain connectivity between AD and normal brains.

Our Main Contributions

• Employ sparse inverse covariance estimation for brain region connectivity identification.

• Develop a novel algorithm for sparse inverse covariance estimation that facilitates the use of domain knowledge.

• Our empirical evaluation on neuroimaging PET data reveals several interesting connectivity patterns consistent with literature findings, and also some new patterns that can help the knowledge discovery of AD.

Sparse Inverse Covariance Estimation

• Given the observations xi~N(μ, Σ), the empirical covariance matrix S is

• We can estimate by solving the following maximum likelihood problem

• By penalizing the L1-norm, we can obtain the sparse inverse covariance matrix

n

i

Tii xx

nS

1

))((1

)tr(detlogmax0

Sf

10)vec()tr(detlogmax

Sf

1

Why Sparse Inverse Covariance?

• The covariance matrix can be estimated robustly when many entries of the inverse covariance matrix are zero.

• The sparse inverse covariance matrix can be interpreted from the perspective of undirected graphical model.– If the ijth component of Θ is zero, then variables i and j are

conditionally independent, given the other variables in the multivariate Gaussian distribution.

• Many real-world networks are sparse.– Gene interaction network

Related Work

• O. Banerjee, L. El Ghaoui, and A. d’Aspremont. Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. Journal of Machine Learning Ressearch, 9:485–516, 2008.

• J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 8(1):1–10, 2008.

• Jianqing Fan, Yang Feng and Yichao Wu. Network exploration via the adaptive Lasso and SCAD penalties. Annals of Applied Statistics, 2009.

Example: Gene Network

Rosetta Inpharmatics Compendium of gene expression profilesdescribed by Hughes et al. (2000)

Example: Senate Voting Records Data (2004-06)

Republican senatorsDemocratic senators

Chafee (R, RI) has only Democrats as his neighbors, an observation that supports media statements made by and about Chafee during those years.

Senator Allen (R, VA) unites two otherwise separate groups of Republicans and also provides a connection to the large cluster of Democrats through Ben Nelson (D, NE), which also supports media statements made about him prior to his 2006 re-election campaign.

Proposed SICE Algorithm

• It estimates the matrix Θ directly.

• User feedback can be incorporated by adding constraints.

• It is based on the block coordinate descent.– Friedman et al. 2008

Block Coordinate Descent

P. Tseng. Convergence of block coordinate descent method for nondifferentiable maximation. J. Opt. Theory and Applications, 109(3):474–494, 2001.

Data Collected

• We used FDG-PET images (49 AD, 116 MCI, 67 NC)• The data were acquired under the support of ADNI

– http://www.loni.ucla.edu/Research/Databases/

Subject ADMale

ADFemale

MCIMale

MCIFemale

NCMale

NCFemale

Number 27 22 76 40 43 24

MeanAge

76.74 75.59 75.84 75.85 76.74 75.33

Std deviationAge

6.75 6.11 6.12 6.08 6.26 5.82

Brain Regions

1 Frontal_Sup_L 13 Parietal_Sup_L 21 Occipital_Sup_L 27 Temporal_Sup_L

2 Frontal_Sup_R 14 Parietal_Sup_R 22 Occipital_Sup_R 28 Temporal_Sup_R

3 Frontal_Mid_L 15 Parietal_Inf_L 23 Occipital_Mid_L 29 Temporal_Pole_Sup_L

4 Frontal_Mid_R 16 Parietal_Inf_R 24 Occipital_Mid_R 30 Temporal_Pole_Sup_R

5 Frontal_Sup_Medial_L 17 Precuneus_L 25 Occipital_Inf_L 31 Temporal_Mid_L

6 Frontal_Sup_Medial_R 18 Precuneus_R 26 Occipital_Inf_R 32 Temporal_Mid_R

7 Frontal_Mid_Orb_L 19 Cingulum_Post_L 33 Temporal_Pole_Mid_L

8 Frontal_Mid_Orb_R 20 Cingulum_Post_R 34 Temporal_Pole_Mid_R

9 Rectus_L 35 Temporal_Inf_L 8301

10 Rectus_R 36 Temporal_Inf_R 8302

11 Cingulum_Ant_L 37 Fusiform_L

12 Cingulum_Ant_R 38 Fusiform_R

39 Hippocampus_L

40 Hippocampus_R

41 ParaHippocampal_L

42 ParaHippocampal_R

Temporal lobeFrontal lobe Parietal lobe Occipital lobe

Experimental result

frontal

AD MCI NC

parietaloccipitaltemporal

frontal, parietal, occipital, and temporal lobes in order

Experimental result

AD MCI NC

frontal, parietal, occipital, and temporal lobes in order

Experimental result

AD MCI NC

frontal, parietal, occipital, and temporal lobes in order

Key Observations: Within-Lobe Connectivity

• The temporal lobe of AD has significantly less connectivity than NC. – The decrease in connectivity in the temporal lobe of AD,

especially between the Hippocampus and other regions, has been extensively reported in the literature.

• The temporal lobe of MCI does not show a significant decrease in connectivity, compared with NC.

• The frontal lobe of AD has significantly more connectivity than NC. – Because the regions in the frontal lobe are typically affected

later in the course of AD, the increased connectivity in the frontal lobe may help preserve some cognitive functions in AD patients.

Key Observations: Between-Lobe Connectivity

• In general, human brains tend to have less between-lobe connectivity than within-lobe connectivity.

• The connectivity between the parietal and occipital lobes of AD is significantly more than NC which is true especially for mild and weak connectivity.– Compensatory effect– K. Supekar, V. Menon, D. Rubin, M. Musen, M.D. Greicius. (2008)

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease. PLoS Comput Biol 4(6) 1-11.

AD MCI NC

frontal, parietal, occipital, and temporal lobes in order

Brain Connectivity for Normal Controls

Brain Connectivity of Mild Cognitive Impairment

Brain Connectivity for AD Patients

The connectivity between the parietal and occipital lobes of AD is significantly more than NC. (help preserve cognitive functions)

Conclusion and Future Work

• Conclusion– Apply sparse inverse covariance estimation to model

functional brain connectivity of AD, MCI, and NC based on PET neuroimaging data.

– Our findings are consistent with the previous literature and also show some new aspects that may suggest further investigation in brain connectivity research in the future.

• Future work– Investigate the connectivity patterns. – Investigate the connectivity of different brain regions using

functional magnetic resonance imaging (fMRI) data.

Thank you!

PET

• Positron emission tomography (PET) is a test that uses a special type of camera and a tracer (radioactive chemical) to look at organs in the body. During the test, the tracer liquid is put into a vein in the arm. The most commonly used for this purpose is a sugar called fluorodeoxyglucose (FDG). The tracer moves through your body, where much of it collects in the specific organ or tissues. The tracer gives off tiny positively charged particles (positrons). The camera records the positrons and turns the recording into pictures on a computer.