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
N Mean
Median
Min Max SD
Ap3 148 49.2 51.5 21.1 81.2 17.2
Ap4 74 52.2 55.4 21.7 79.1 16.1
Total 222 50.2 52.1 21.1 81.2 168
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
S
elec
t gr
oup
spat
ial m
aps:
Hierarchical clustering of the connectivity matrix across subjects
Hierarchical clustering of the connectivity matrix across subjects
VisualMotorDMN
Hierarchical data-driven clustering reveals/recovers large-scale brain networks
Visual Motor
The connectivity matrix (across subjects)
Full
corr
elati
ons
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
corr
elati
ons
Direct links
Direct + indirect links
The connectivity matrix (across subjects)
Partial correlations (ICOV, lambda=10)
Full
corr
elati
ons
Direct links
Direct + indirect links
Full correlation L=0 (no regularization)
L=5 L=10
-50 50T-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)
-10 10
T values
10T values
10
Age
0
0
Age
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 linksDirect links
Modelling effects of age (edges showing abs(tage>7))
Network modelling
Modelling effects of APOE
Ap3Age > Ap4AgeAp3 > Ap4 Small effects compared to the age effects!
Modelling effects of APOE (edges showing abs(tgroup>2.5))
Network modelling (Ap3>Ap4) – NB! Multiple comparisons
Modelling effects of APOE by age interactions (edges showing abs(tgroup_by_age>2.5))
Network modelling (Ap3Age>Ap4Age) – NB! Multiple comparisons
Modelling effects of APOE by age interactions (edges showing abs(tgroup_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!