learning brain connectivity of alzheimer's disease from neuroimaging data

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Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data Shui Huang 1 , Jing Li 1 , Liang Sun 1 , Jun Liu 1 , Teresa Wu 1 , Kewei Chen 2 , Adam Fleisher 2 , Eric Reiman 2 , Jieping Ye 1 1 : Arizona State University, 2 : Banner Alzheimer’s Institute This work was sponsored by the NSF. • Literature suggests functional brain connectivity difference between AD and normal aging. • Existing functional connectivity studies have limitations: -Correlation-based methods capture only pair-wise info. -Most approaches are confirmative, not exploratory. -A small number of brain regions are focused on. -Thorough comparison between AD, MCI, and normal aging with statistical significant assessment is lacking. -fMRI data are mostly used, not PET Introduction Sparse Inverse Covariance Estimation (SICE) 1 0 θ || ) θ ( || ) θ ( )) θ log(det( max arg θ ˆ vec S tr ) ( 1 k C ) ( 2 k C k X 1 2 2 1 ) ( ) ( 2 1 k k C C Objective • Build functional brain connectivity models of AD, MCI, and normal controls using a machine learning technique, called inverse covariance, based on ADNI-PET data. • Assess statistical significance of the connectivity difference and summarize the results. Approach & Monotone Property Monotone Property Let and be the sets of all the connectivity components of with and respectively. If , then . Intuitively, if two regions are connected (either directly or indirectly) at one level of sparseness, they will be connected at all lower levels of sparseness. Results Sm all λ Large λ λ 3 λ 2 λ 1 Sm all λ Largeλ AD MCI NC • AD: between- lobe connectivity weaker than within- lobe con. • AD: left-right same region connectivity much weaker. MCI: patterns not as distinct from normal controls as AD. Observatio ns: AD MCI NC AD MCI NC AD MCI NC Strong Connectivity Mild Connectivity Weak Connectivity •Temporal: decreased connectivity in AD, decrease not significant in MCI. • Frontal: increased connectivity in AD (compensation), increase not significant in MCI. • Parietal, occipital: no significant difference. • Parietal-occipital: increased weak/mild con. in AD. • Frontal-occipital: decreased weak/mild con. in Observatio ns:

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Literature suggests functional brain connectivity difference between AD and normal aging. Existing functional connectivity studies have limitations: -Correlation-based methods capture only pair-wise info. -Most approaches are confirmative, not exploratory. - PowerPoint PPT Presentation

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Page 1: Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data Shui Huang1, Jing Li1, Liang Sun1, Jun Liu1, Teresa Wu1, Kewei Chen2, Adam Fleisher2, Eric Reiman2, Jieping Ye1

1: Arizona State University, 2: Banner Alzheimer’s Institute

This work was sponsored by the

NSF.

• Literature suggests functional brain connectivity difference between AD and normal aging.• Existing functional connectivity studies have limitations: -Correlation-based methods capture only pair-wise info. -Most approaches are confirmative, not exploratory. -A small number of brain regions are focused on. -Thorough comparison between AD, MCI, and normal aging with statistical significant assessment is lacking. -fMRI data are mostly used, not PET

Introduction

Sparse Inverse Covariance Estimation (SICE)

10θ

||)θ(||)θ())θlog(det(maxargθ̂ vecStr

)( 1kC )( 2kC

kX 1 2 21 )()( 21 kk CC

Objective

• Build functional brain connectivity models of AD, MCI, and normal controls using a machine learning technique, called inverse covariance, based on ADNI-PET data.• Assess statistical significance of the connectivity difference and summarize the results.

Approach & Monotone Property

Monotone PropertyLet and be the sets of all the connectivity components of with and respectively. If , then .Intuitively, if two regions are connected (either directly or indirectly) at one level of sparseness, they will be connected at all lower levels of sparseness.

Results

Small λLarge λ λ3 λ2 λ1Small λLarge λ

AD MCI NC

• AD: between-lobe connectivity weaker than within-lobe con.• AD: left-right same region connectivity much weaker.• MCI: patterns not as distinct from normal controls as AD.

Observations:

AD MCI NC AD MCI NC AD MCI NC

Strong Connectivity Mild Connectivity Weak Connectivity

•Temporal: decreased connectivity in AD, decrease not significant in MCI.• Frontal: increased connectivity in AD (compensation), increase not significant in MCI.• Parietal, occipital: no significant difference.• Parietal-occipital: increased weak/mild con. in AD.• Frontal-occipital: decreased weak/mild con. in MCI.• Left-right: decreased strong con. in AD, not MCI.

Observations: