1 / 23

ICA of Functional MRI Data: An Overview

ICA of Functional MRI Data: An Overview. V.D. Calhoun, T. Adali, L.K. Hansen, et al., ICA 2003 Symposium. Paper Presentation by Avshalom Elyada February 2004. Functional MRI. Non-invasively measure brain activity

kylie-morrison
Télécharger la présentation

ICA of Functional MRI Data: An Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ICA of Functional MRI Data:An Overview V.D. Calhoun, T. Adali, L.K. Hansen, et al., ICA 2003 Symposium Paper Presentation by Avshalom ElyadaFebruary 2004

  2. Functional MRI • Non-invasively measure brain activity • Most popular method observes neuron activity indirectly by measuring Vascular and Hemodynamic signals • Change in blood flow and oxygenation (oxygenated vs. deoxygenated blood) in active brain areas is measured using MRI ICA of fMRI

  3. Data Acquisition • Two orthogonal detectors capture MRI signal • This two channel input is put in complex form:f(t) + ig(t) • Discrete Fourier transform of this time-domain data yields complex image-space data • Usually magnitude only is used (but it is shown that ignoring phase loses significant data) • Data is in form of small intensity changes over time (contrast-to-noise ratio < 1) ICA of fMRI

  4. Infomax BSS Algorithm • Widely used separation algorithm in fMRI • Neural Network viewpoint: • The algorithm is based on maximizing the output entropy (or information flow) of a neural network with non-linear outputs • Actually it is equivalent to Maximum-Likelihood, as we shall touch upon later ICA of fMRI

  5. Signals of Interest • Task-related • Such as visual, visuomotor, … • Transiently task-related • Some components of brain response to task vary over time (stop before stimulation stops, change when repeated stimuli applied …) • Function-related • Several different transiently task-related signals may come from different areas when a certain function performed (e.g. correlation between opposite brain sides.) ICA of fMRI

  6. Signals Not-of-interest • Physiology-related • Such as breathing, heart-rate • Motion-related • For instance when performing speaking experiment, signals due to mouth movement are detected ICA of fMRI

  7. Noise • Magnetic resonance • Patient movement • Different from motion-related : here patient movement causes measurement noise • Physiological (heart-rate, breathing) • Again, note difference from prev. slide: here we refer to measurement noise and not the breathing related activity in the brain. ICA of fMRI

  8. Visual Stimulation ICA Analysis Task related Heart beat & breathing related Low-freq. component possibly related to vasomotor oscillation Motion related “white noise” ICA of fMRI

  9. Statistical Properties • For ICA, sources must be non-Gaussian with spatial & temporal independence • fMRI signals are typically focal and thus have sub-Gaussian spatial distribution • Noise generally non-Gaussian • If sources don’t have systematic overlap in time and/or space then considered independent ICA of fMRI

  10. Spatial Correlation • The hemodynamic signal being measured is not the signal of interest itself, but an indirect indication • It has a spatial point spread function • Due to the hemodynamic properties themselves • Can be affected by choice of measurement parameters (sensitivity to blood flow and oxygenation, magnetic sensitivity) ICA of fMRI

  11. Temporal Correlation • Can be introduced by rapid sampling, • By temporal hemodynamic point spread function, • Or by poorly understood temporal autocorrelations in the data ICA of fMRI

  12. Spatial vs. Temporal • ICA is used to understand spatio-temporal structure of the fMRI signal • Factor the data into a product of a set of time courses and a set of spatial patterns • PCA: orthogonal time courses vs. orthogonal spatial patterns • ICA: neither are assumed a priori independent • Spatial ICA: Spatial independence is the leading assumption, followed by temporal • Temporal ICA: vice versa ICA of fMRI

  13. ICA Block Model ICA of fMRI

  14. Choice of Algorithm • Depends on assumptions about signals of interest • Spatial or temporal independence • Sub- or super-Gaussian sources • Evaluate effectiveness of algorithms, variants, preprocessing, check divergence to “true” distribution • Problem, since true distribution unknown • One method: Hybrid fMRI experiment. Superimposing a known source on the real fMRI data, check effectiveness of reconstructing known source (hybrid fMRI experiment). ICA of fMRI

  15. Infomax • Entropy H • Mutual Information I • Infomax: minimize I between the sources • But minimizing I is hard • maximize entropy instead (both are indications of signal i.i.d) ICA of fMRI

  16. Infomax (II) • Assume X is input to Neural Network, whose outputs are WiTX • Wi the weight vectors of the neurons • Infomax: maximize entropy of outputs • Plain max. not possible (-H can go to inf), maximize for gi(WiTX) , gi some non-linear scalar functions: H[g1(W1TX), … , gn(WnTX)] • This model can be used for ICA if gi are well-chosen ICA of fMRI

  17. Connection Between Infomax and ML • The two approaches are equivalent • proof: “Infomax & ML for BSS” / JF Cardoso • Log-Likelihoodexpectation • If fi equal to actual dist. func., then first term above equal to ∑iH[WiTX] • Hence ML = -H + Constant, • Mazimize entropy  minimize likelihood • Choose gi close as possible to fi • As in ML, gi need not be known, only gaussianity ICA of fMRI

  18. Group ICA • We aim to draw inferences about groups of signals, then plot them together • In ICA, different individuals in the group may have different time courses • An approach recently developed performs statistical comparison of individual maps trying to estimate the time-course parallelism ICA of fMRI

  19. Non Task Related Left&right task-related to visual stimuli Sensitive to changes in stimuli (Transiently Task Related) ICA of fMRI

  20. Comparisons • For example comparing a visual task with a visuo-motor task • Use a priori template to extract components of interest • Conjunctive ( & ), Subtractive ( - ) ICA of fMRI

  21. Visual Vs. Visuomotor Comparison Conjunction V & VM:Visual areas appear Subtraction VM - V:Motor areas appear ICA of fMRI

  22. Use of a priori Information • Provide improved separability • For example extract one component for selective analysis • ICA model make assumptions about the sources • A priori templates help assess impact of assumptions • Validation • Difficult, since sources are unknown • Hybrid fMRI experiment as mentioned earlier ICA of fMRI

  23. Reference • ICA of functional MRI data: An Overview • Calhoun, Adali et. al, ICA2003 • Infomax and ML for BSS • Jean-Francois Cardoso • ICA Tutorial • Aapo Hyvärinen, Erkki Oja • www.cis.hut.fi/aapo/papers/IJCNN99_tutorialweb/IJCNN99_tutorial3.html ICA of fMRI

More Related