real-time independent component analysis of functional mri time-series

Brain Innovation BV Turbo BrainVoyager Training Course January, 2011

Real-time Independent Component Real-time Independent Component Analysis of functional MRI time-seriesAnalysis of functional MRI time-series

A new TBV (3.0) Plugin for A new TBV (3.0) Plugin for Real-Time ICA during fMRIReal-Time ICA during fMRI


Real-time ICA of fMRI data: Outline• Data model and analysis tools in real-time fMRI:

• Sliding-window vs Cumulative approaches

• Data-driven analysis tools in fMRI:• Component-based generative models for fMRI• Spatial independent component analysis (s-ICA)

• Real-time (spatial) Independent Component Analysis• Data model and implementation• The “Sliding-window” FastICA algorithm• Perfomances, operation and user interface

• Examples of applications• Motor activity, Auditory and emotional activity during music listening

• A New “plug-in” for Turbo BrainVoyager 3.0• Example of application for visual activity monitoring


Data Analysis Tools for Real-time fMRI (1)

• Real-time fMRI enables one to monitor a subject’s brain activities during an ongoing session:– Results are to be delivered (and used) in/near real-time, i. e.

within times in the order of one (or a few) TR(s) ...

• Trade-off between accuracy VS computational times:– > Minimum batch of temporal observations [# time points] to

generate a reliable activation map (statistical power)– > Minimum time window size [s] to cover the essential

dynamics of the activaiton (hemodynamics, stimulus changes, ...)


• Real-time fMRI enables one to monitor a subject’s brain activities during an ongoing session:– Results are to be delivered (and used) in/near real-time, i. e.

within times in the order of one (or a few) TR(s) ...

• Trade-off between accuracy VS computational times:– < Maximum batch of temporal observation to generate the

activation map in real-time (bottleneck: computational load)– < Maximum time window size [s] to promptly detect transient

(or temporally nonstationary) dynamic effects before these become “irrelevant” and sacrificed in favor of more repetitive and temporally stationary effects (Mitra and Pesaran, 1999).



• Real-time fMRI utilizes two different approaches:– cumulative window (Cox et al., 1995)– sliding window (Gembris et al., 2000; Posse et al., 2001)

• In the cumulative approach:– the entire partially measured fMRI time-series is analyzed in

one step. One edge of the time window is fixed, whereas the other moves during the acquisition of new data.

– the specificity (wrt repetitive/stationary effects) increases over time (more data become available for “averaging”).

– The sensitivity (wrt transient/non-stationary effects) is reduced (more fluctuations become relevant)

– The computational load increases over time (unless spatial or temporal resolution is sacrificed!)



• Real-time fMRI utilizes two different approaches:– cumulative window (Cox et al., 1995)– sliding window (Gembris et al., 2000; Posse et al., 2001)

• In the sliding-window approach:– The analysis is restricted to the most recently acquired data.

Both edges of the window move during the acquisition.– The accuracy is constant over time and the sensitivity to

dynamic changes in brain activity can be maximized.– The specificity is limited and critically dependent on SNR– The computational load is constant


Time [Units of TR]titi-L+1

Sliding Window

OFF

ON

(a) Cumulative Read-out(b) Dynamic Read-out

Target Area

Target Signal

t1

Esposito et al., Neuroimage 2003


Data-driven tools in Real-time fMRI (1)

• Off-line, data-driven tools nicely and usefully complement by hypethesis-driven analysis tools

• E. g., independent component analysis (ICA) can identify brain activity without a priori “temporal” assumptions on brain activity:– No info about experimental paradigm (stimulus)– No detailed information about hemodynamics– “Rough” knowledge of potentially relevant areas– ...


Data-driven tools in Real-time fMRI (2)

• Real-time fMRI data analysis is traditionally based solely on hypothesis-driven tools (e. g. GLM) because data-driven tools (such as ICA) are:– computationally demanding (time consuming)– difficult settings (options, contrains and constants)

• e. g. convergence problems (no result delivering)

– difficult selection of the results– “post-hoc” (complex) interpretation– ...


Component-based Generative Models (1)

C#1

C#2

C#3

C#n

Measured fMRI time-series

Time (scans)

Brain Innovation BV Turbo BrainVoyager Training Course

DATA (Y)

time

voxels time

voxelsC1j

C2j

...

Cnj

COMPONENTS (C)W-1(A)

Yj Ai Al

Y AC C WYMixing Unmixing

High statistical dependencies Low statistical dependencies

Component-based Generative Models (2)


• Maximum variance principle (VARIMAX):

(1): time-courses must be also orthogonal (uncorrelated)

(2): components ordered by relative contribution to variance

• Orthogonality Principle (simple linear decorrelation):

M

kjkikji CCCC

1

0 ji

1

( ) maxM

i ikk

Var C C

Principal Component Analysis


• Independency Principle (non-linear decorrelation):

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kkkn CpCCCp

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k

qjk

pik CC

1

0

• Information Theory: Minimization of mutual information

• Maximize entropy flow of a neural network: H(C) -> max (Infomax)

• Maximize Non-gaussianity of components: N(C) -> max (Fastica)

• Statistical dependency is removed along one dimension (e.g. space):

(1): time-courses can be correlated (spatial ICA)

(2): components not ordered by relative contribution to variance

Independent Component Analysis (1)


Formisano, et al., Magnetic Resonance Imaging 2004

ICA vs PCA


• (Like PCA) ICA requires the computation of the data covariance matrix of the voxels’ time courses included in the analysis

• (Unlike PCA) spatial ICA only models the spatial distributions of brain activities (and builds accordingly the output maps)– What ICA “offers” in addition to PCA does not depend on the

covariance but only the spatial statistics

• While the statistical power of covariance estimation depends on the temporal window of observation (and the number of time points), the power of the spatial distribution estimation only depends on the voxel space

Independent Component Analysis (2)


Signal

Noise (pure)

Features

The “power” of spatial statisistics (1)


Z-score (activation parameter)

The “power” of spatial statisistics (2)


• The computational load of spatial ICA algorithms grows much more with the temporal dimension than with the number of voxels included in the analysis

• If we “fix” the temporal window the power of spatial statistics is constant. If the temporal window is large enough to ensure enough accuracy of the maps, the computation load can be held constant in a sliding-window approach

• In order to deliver components as fast as possible a “deflation” scheme can be used to extract ICA components one by one (FastICA algorithm by Hivarinen 1999). This renders the ICA component maps immediately available even in the presence of convergence problems.

Real-time ICA (1)


The FastICA algorithm

1

TT

ij it tj i jt

C w X w X

1...T

n C w w X WX

maxTi jJ w X

maxn

Ti j

i

J w X

T Tj i j k j ikE w X w X

“one-unit” function

“multi-unit” functionsymmetric

deflation


Real-time ICA (2)

• Rt-ICA -> sliding-window approach + FastICA• The window is chosen to solve the trade-off between

accuracy and computational load.• This approach works and can be useful if:

1. FastICA delivers useful and accurate components among the “first” extracted ICs in a relatively low number of iteration per run. If not, we cannot assume “no activity”

2. The selection can be aided and supported by (rough) prior knowledge about where activity of interest takes place but selectivity should be unambigous

3. Cumulative maps about a process of interest can be obtained by adequately tracking over time (and combining) subsequent sliding-window ICA components

Time [Units of TR]titi-L+1

Sliding Window

OFF

ON

(a) Cumulative Read-out(b) Dynamic Read-out

Target Area

Target Signal

t1


Tim

e of

ext

ract

tion

[s]

Subject FE

unsmoothed images

smoothed images

Subject FDS

unsmoothed images

smoothed images

Subject AA

unsmoothed images

smoothed images

ICA G5

ICA G3


#46…...

#51.…..

#55.…..

#41……

scans

1 2 3 4 5

Dynamic Maps Cumulative Map and Time-course

Frames

scans

scans

scans

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#52

#42

#48

#53

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#43

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#44

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Normalized Signal Change






ICA in real-time fMRI during visual stimulation:ICA in real-time fMRI during visual stimulation:A new plugin for Turbo Brain Voyager 3.0A new plugin for Turbo Brain Voyager 3.0



RTICA PLUGINMAP VIEWER

NeuroFeedback(MAP ANALYZER)

TBV LOG

Real time ROISelection

IncomingData

Ranked ICAComponent Maps

Data PointerICA Component Rankings

Spatial correlationsand/or

other relevant parameters


Real-time ICA of fMRI data: Conclusions

• Real-time ICA during fMRI is feasible in many circumanstances and has some potentials in monitoring brain activity under typical real-time fMRi settings

• The Sliding-window fastICA algorithm has comparable performances to GLM under highly controlled situations but requires no timing information and no critical settings

• This opens the possibility of monitoring non-triggered, non-repetitive and non-stationary neural activity with only mininal spatial prior on the networks involved

• Integration of rt-ICA generated maps in neurofeedback experiments now possible with the new Plugin for TurboBrainVoyager 3.0


Thank [email protected]

real-time independent component analysis of functional mri time-series

Documents

datadriven tools

power of spatial statistics

spatial statisticswhile

spatial ica2

analysisunlike pca spatial

fmri data analysis

pca ica

brain activity