Network modelling using resting-state fMRI: effects of age and APOE

1. Network modelling using resting-state fMRI: effects of age and APOE Lars T. Westlye University of Oslo CAS kickoff meeting 23/8-2011

2. Patternsofbrainactivation during rest The brain is not primarily reflexive Whilst part of what we perceive comes through our senses from the object before us, another part (and it may be the larger part) always comes out of our own head(William James, 1890) Functional networks Hierarchical clustering The resting brain is highly organized into functional hierarchical networks

3. Independent component analysis (ICA) A computational method forseparating a multivariate signal into additive and statistically independent (though not necessarily orthogonal) subcomponents. Originally proposed to solve blind source separation or so-called cocktail-party problems: Allows blind separation of N sound sources summed in recordings at N microphones, without relying on a detailed model of the sound characteristics of each source or the mixing process.

4. ICA Example: Speech Separation Courtesyof dr. Arno Delorme, UCSD

5. Typically, brain imaging data are high-dimensional and multivariate in nature, i.e. the estimated signal could be regarded as a mixture of various independent sources The case of EEG

6. Spatial group ICA on temporally concatenated FMRI data Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) Beckmann et al.

7. The various IC spatial maps reflect intrinsic patterns of functional organization across subjects and correspond with known neuroanatomical and functional brain ”networks” Veer et al., 2010

8. How do we get from the group level to the subject level?

9. Dual regressionallows for estimationsofsubect-specific spatial maps and corresponding time courses Spatiotemporalregression in twosteps: A) Use the group-level spatial maps as spatial regressors to estimate the temporal dynamics (time courses) associated with each gICA map B) Use time courses (after optional normalization to unit variance) spatial regressors to find subject-specific maps associated with the group-level maps.

10. Spatiotemporalregression: Yielding n by d time courses, wheren=numberofsubjects and d=model order (numberofICs). Imagingphenotype: The covarianceofthe time coursesreflectthelarge-scalefunctionalconnectivityofthebrain, and can be submitted to variousconnectivityanalysis - includinggraphtheoreticalapproaches and othervarietisofnetworkmodeling - and subsequentanalysiswith relevant demographic, cognitive and genetic data.

11. Application: Modellingtheeffectsof age and APOE Imaging data: 1.5 T Siemens Avanto, 10 min restingstatefMRI (200 TRs) Conventional preprosessing includingmotioncorrection, filteringetc Group ICA (on 94 subjects to avoid bias due to age and genotype) using temporal concatenation in melodic (d=80) and dual regression in order to estimatesubject-specific time coursesofeach IC. Exclusionof 43 ICsreflectingmotionartefacts, pulsationetcyielded 36 restingstatenetworks (RSNs)

12. Questions: 1) Is the covariance between the time courses influenced by age? 2) Is the covariance between the time courses influenced by APOE status? Main effects (Ap3 vs Ap4) and group by age interactions (are the age slopes comparable across groups?) modelled using ANCOVAs Select group spatial maps:

13. Hierarchicalclusteringoftheconnectivitymatrixacrosssubjects

14. Hierarchicalclusteringoftheconnectivitymatrixacrosssubjects DMN Motor Visual

15. Hierarchicaldata-drivenclusteringreveals/recoverslarge-scalebrainnetworksHierarchicaldata-drivenclusteringreveals/recoverslarge-scalebrainnetworks Visual Motor

16. The connectivitymatrix (acrosssubjects) Partialcorrelations (ICOV, lambda=10) (see Smith et al., 2010, NeuroImage) Full correlations

17. The connectivitymatrix (acrosssubjects) Partialcorrelations (ICOV, lambda=10) (see Smith et al., 2010, NeuroImage) Direct links Full correlations Direct + indirect links

18. The connectivitymatrix (acrosssubjects) Partialcorrelations (ICOV, lambda=10) Direct links Full correlations Direct + indirect links Full correlation L=0 (no regularization) L=5 L=10 -50 50 T-values

19. The connectivitymatrix (acrosssubjects) Network modellingusing full correlation (strongestedgesshownonly)

20. The connectivitymatrix (acrosssubjects) Network modellingusingicov (L=10) (strongestedgesshownonly)

21. Modellingeffectsof age and APOE Age Ap3>Ap4 (parallell slopes) Ap3>Ap4 (separate slopes) Ap3Age > Ap4Age -10 0 10 T values Subject-specificconnectivitymatrices (n=222) Age Ap3>Ap4 (separate slopes) Ap3>Ap4 (parallell slopes) Ap3Age > Ap4Age 0 10 10 T values

22. Modellingeffectsof age Direct links Direct + indirect links

23. Modellingeffectsof age (edgesshowingabs(tage>7)) Network modelling

24. Modellingeffectsof APOE Ap3 > Ap4 Small effects compared to the age effects! Ap3Age > Ap4Age

25. Modellingeffectsof APOE (edgesshowingabs(tgroup>2.5)) Network modelling (Ap3>Ap4) – NB! Multiple comparisons

26. Modellingeffectsof APOE by age interactions (edgesshowingabs(tgroup_by_age>2.5)) Network modelling (Ap3Age>Ap4Age) – NB! Multiple comparisons

27. Modellingeffectsof APOE by age interactions (edgesshowingabs(tgroup_by_age>2.5)) Althoughsmalleffectsizes, all ”significant” edgespointpoint in the same direction

28. Alternative to the univariate edge-analyses: Edge-ICA

29. Alternative to the univariate edge-analyses: Edge-ICA Perform temporal ICA on the edges (one connectivity matrix per subject) Subject-specificconnectivitymatrices (n=222) Transpose matrices ICA Correlate with age/APOE

30. Alternative to the univariate edge-analyses: Edge-ICA Edge-ICA #2

31. Future possibilities? Integrating measures of structural connectivity (DTI) and integrity (cortical thickness, surface area) e.g. using linked ICA (Groves et al., 2010) Assessing the between subject/sample reliability of the various measures Implementing graph-theoretical procedures (Sporns et al) etc

32. Thanks!