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Joint Detection-Estimation of Brain Activity in fMRI using Graph Cuts Thesis for the Master degree in Biomedical Engineering Lisbon, 30 th October 2008 Joana Maria Rosado da Silva Coelho

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Contents: 1Introduction 2Objectives 3Proposed Model 4Results 5Conclusions

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fMRI technique aims at identifying cerebral areas (Brain Mapping) that were activated by an external stimulus – paradigm. Classical tasks to induce neuronal responses: visual activation (looking at changing patterns); sensorimotor activation (sequence of defined finger movements). This modality is based on the assumption that activated regions present increased metabolic activity. functional MRI:

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The higher proportion of hemoglobin molecules bound with oxygen (oxyhemoglobin) is observed as a signal increase on T2*- weighted images (increase in the BOLD signal). BOLD signal does not measure brain function directly. fMRI-BOLD BaselineActivation

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After a stimulus application there is a local hemodynamic change in capillaries and draining veins. This vascular response can be modeled by an hemodynamic response function. Hemodynamic Response Function

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Diagram of a typical fMRI data set. Type of Data:

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Example of a time course from a visual stimulation experiment. Type of Data:

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Classical method: - Statistical parametric mapping (SPM) commonly based on GLM - 2 steps algorithm: Estimation and Inference - Inference step needs the tuning of the p-value SPM-GLM

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Contents: 1Introduction 2Objectives 3Proposed Model 4Results 5Conclusions

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Objectives: Estimate the hemodynamic response function (HRF) Incorporate the drift removal Statistical Model: SPM-Drift-GC Model spatial correlation Detect activated regions

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Contents: 1Introduction 2Objectives 3Proposed Model 4Results 5Conclusions

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Proposed Model: Neuro- Hemodynamic System StimuliBOLD Signal

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Bayesian Approach – MAP criterion The Maximum a Posteriori (MAP) estimation is obtained by computing where Data Fidelity Term Prior Term

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Algorithm For each voxel, the estimation of b i, h i and d i is performed iteratively. h 0 is a gamma function as proposed by Friston et al in 1998 which provides a physiological reasonable waveform to the HRF.

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The final step models spatial correlation. Since different tasks activate different brain regions, it is less probable that non-activated voxels appear inside of an activated region and the converse is also true. Avoids misclassification inside activated regions. Energy function: D p – cost of attributing the label to the pixel p V h,v – cost of attributing the labels, to the N neighbour pixels Spatial correlation step

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Contents: 1Introduction 2Objectives 3Proposed Model 4Results 5Conclusions

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Results – Synthetic data SPM-DriftSPM-Drift-GC

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Results – Synthetic data Example of an SNR=2 dB time course with the real and estimated drift.

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Motor task – Right foot SPM-GC-Drift Loose result SPM-GLM Reference result SPM-GLM Restrict result SPM-GLM Results – Real data

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Verb generation task Loose result SPM-GLM Reference result SPM-GLM Restrict result SPM-GLM Results – Real data SPM-GC-Drift

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Motor task – Tongue Loose result SPM-GLM Reference result SPM-GLM Restrict result SPM-GLM Results – Real data SPM-GC-Drift

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Verb generation task Loose result SPM-GLM Reference result SPM-GLM Restrict result SPM-GLM Results – Real data SPM-GC-Drift

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Results – Real data

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Contents: 1Introduction 2Objectives 3Proposed Model 4Results 5Conclusions

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The Bayesian framework combined with Graph Cuts algorithm improves the sensitivity in the detection of activated areas. The proposed algorithm does not require the tuning of any parameter by the clinician. The beta coefficients are considered to be binary. SPM-GC-Drift leads to similar results as the ones obtained by SPM-GLM. However, other brain activated regions were also detected which requires future analysis. Conclusions

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The present work has been published 30th Annual International IEEE EMBS Conference in Vancouver, British Columbia, Canada RecPad2008 – 14ª Conferência Portuguesa de Reconhecimento de Padrões … and submitted Human Brain Mapping international journal Thank you for your attention!!! Conclusions

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