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

The brain is not primarily reflexive Patterns of brain activation during rest The resting brain is highly organized into functional hierarchical networks Hierarchical clusteringFunctional networks 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)

Independent component analysis (ICA) A computational method for separating 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.

Example: Speech Separation ICA Courtesy of dr. Arno Delorme, UCSD

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

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

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

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

Dual regression allows for estimations of subect-specific spatial maps and corresponding time courses Spatiotemporal regression in two steps: 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.

Spatiotemporal regression: Yielding n by d time courses, where n=number of subjects and d=model order (number of ICs). Imaging phenotype: The covariance of the time courses reflect the large-scale functional connectivity of the brain, and can be submitted to various connectivity analysis - including graph theoretical approaches and other varietis of network modeling - and subsequent analysis with relevant demographic, cognitive and genetic data.

Application: Modelling the effects of age and APOE NMea n Medi an MinMaxSD Ap Ap Total Imaging data: 1.5 T Siemens Avanto, 10 min resting state fMRI (200 TRs) Conventional preprosessing including motion correction, filtering etc 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 estimate subject- specific time courses of each IC. Exclusion of 43 ICs reflecting motion artefacts, pulsation etc yielded 36 resting state networks (RSNs)

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:

Hierarchical clustering of the connectivity matrix across subjects

VisualMotorDMN

Hierarchical data-driven clustering reveals/recovers large- scale brain networks VisualMotor

The connectivity matrix (across subjects) Full correlations Partial correlations (ICOV, lambda=10) (see Smith et al., 2010, NeuroImage)

The connectivity matrix (across subjects) Partial correlations (ICOV, lambda=10) (see Smith et al., 2010, NeuroImage) Full correlations Direct links Direct + indirect links

The connectivity matrix (across subjects) Partial correlations (ICOV, lambda=10) Full correlations Direct links Direct + indirect links Full correlationL=0 (no regularization)L=5L= T- values

The connectivity matrix (across subjects) Network modelling using full correlation (strongest edges shown only)

The connectivity matrix (across subjects) Network modelling using icov (L=10) (strongest edges shown only)

Modelling effects of age and APOE Subject-specific connectivity matrices (n=222) T values 10 T values 10 Age 0 0 Ap3>Ap4 (parallell slopes)Ap3>Ap4 (separate slopes) Ap3Age > Ap4Age Ap3>Ap4 (parallell slopes) Ap3>Ap4 (separate slopes) Ap3Age > Ap4Age

Modelling effects of age Direct + indirect links Direct links

Modelling effects of age (edges showing abs(t age >7)) Network modelling

Modelling effects of APOE Ap3Age > Ap4Age Ap3 > Ap4 Small effects compared to the age effects!

Modelling effects of APOE (edges showing abs(t group >2.5)) Network modelling (Ap3>Ap4) – NB! Multiple comparisons

Modelling effects of APOE by age interactions (edges showing abs(t group_by_age >2.5)) Network modelling (Ap3Age>Ap4Age) – NB! Multiple comparisons

Modelling effects of APOE by age interactions (edges showing abs(t group_by_age >2.5)) Although small effect sizes, all ”significant” edges point point in the same direction

Alternative to the univariate edge-analyses: Edge-ICA

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

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

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

Thanks!