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Functional and structural imaging in neurodegenerative diseases Caroline Sage Promotor: Prof. Dr. Stefan Sunaert Co-promotor: Prof. Dr. Wim Robberecht.

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Presentation on theme: "Functional and structural imaging in neurodegenerative diseases Caroline Sage Promotor: Prof. Dr. Stefan Sunaert Co-promotor: Prof. Dr. Wim Robberecht."— Presentation transcript:

1 Functional and structural imaging in neurodegenerative diseases Caroline Sage Promotor: Prof. Dr. Stefan Sunaert Co-promotor: Prof. Dr. Wim Robberecht

2 Overview Introduction Aims and methods Results Future directions

3 Overview Introduction Aims and methods Results Future directions

4 Introduction Neurodegenerative diseases –Alzheimer’s disease –Parkinson’s disease –Multiple sclerosis –Huntington’s disease –Pick’s disease –Prion diseases –Amyotrophic lateral sclerosis

5 Introduction Amyotrophic Lateral Sclerosis (ALS) –Cause is poorly understood 5-10% familial ALS (fALS) 90-95% sporadic ALS (sALS) –Loss of motor neurons (MN) Upper MN signs Lower MN signs –Spectrum disease?

6 Introduction Research in ALS –Cell cultures & molecular research Neuronal cells: motor neurons Non-neuronal cells: astrocytes, oligodendrocytes, microglia Agents for survival and neuronal protection (VEGF,...) –Animal studies Mutant SOD1 mice and rats: overexpression of mutant SOD1 –Pathological mechanisms: glutamate excitotoxicity, impaired axonal transport,... –Rescue experiments –Human studies Ex-vivo: autopsy of brain and/or spinal cord Tissue studies: blood analysis, CSF analysis In-vivo: PET, TMS, 1H-MRS, MRI Pubmed search dd 26/06/2007: 4568 scientific publications, in English, over the last 10 years!

7 Introduction Magnetic resonance imaging (MRI) in ALS –Conventional MRI PD/T2w/FLAIR: non specific markers (Cheung et al., 1995; Hecht et al., 2001; Hecht et al., 2002) T1w: loss of GM volume and to a lesser degree also loss of WM volume, especially in patients with cognitive deficits (Ellis et al., 1999; Abrahams et al., 2005; Grosskreutz et al., 2006) –Functional MRI (fMRI) Motor tasks: recruitment of motor and non-motor areas in ALS patients (Konrad et al., 2002; Schoenfeld et al., 2005) Cognitive tasks: cognitive deficits in ALS patients, especially in ALS patients with concomittant frontotemporal lobe dementia (Abrahams et al., 2006) –Diffusion tensor imaging (DTI) Impairment of the corticospinal tract: reduction of FA and/or increase of D av (Ellis et al., 2001; Toosy et al., 2003; Graham et al., 2004; Hong et al., 2004; Sach et al., 2004; Abe et al., 2005)

8 Overview Introduction Aims and methods Results Future directions

9 Aims Research questions –Are there structural MRI changes in the brain of ALS patients? –Are there functional MRI changes in the brain of ALS patients?  Search for radiological correlates of structural and/or functional deficits in ALS patients by comparing ALS patients with a group of healthy age- and sex-matched controls  Design scan protocol of different tests for use in clinical settings –Improve diagnosis –Provide prognosis –Monitor newly developed therapies

10 WM architecture Diffusion tensor imaging (DTI) Neuronal function fMRI motor tasks Cerebral vasculature/perfusion Dynamic contrast- enhanced T2*w imaging (PWI) Cerebral vasoreactivity fMRI vasoreactivity (VASC)

11 Overview Introduction Aims and methods Results Future directions

12 DTI

13 DTI - introduction DTI –Diffusion Tensor Imaging –Assess Brownian motion of water molecules free diffusion restricted diffusion isotropy anisotropy

14 DTI - introduction Data acquisition –Apply magnetic field gradients in multiple non- collinear directions during MRI data acquisition -> ° signal loss due to diffusion (Stejskal and Tanner, 1965)  Determine diffusion coefficient D in each voxel by varying b-value  In case of highly ordered structures: model diffusion by estimation of diffusion tensor D using multivariate fitting S = S 0 e -bD

15 DTI - introduction non diffusion- weighted image (b0) + ≥ 6 diffusion weighted images DxxDxyDxz DyxDyyDyz DzxDzyDzz λ λ λ 3   

16 DTI - introduction Derive quantitative diffusion parameters –D av : amount of directionally averaged diffusion (in mm²/s) –FA : scalar measure of amount of anisotropy (0 = isotropic; 1 = diffusion in 1 specific direction only) D av =                                    FA =

17 DTI - introduction

18 Mori et al., 1999

19 DTI - aim Study white matter integrity in the brain of ALS patients by means of DT-MRI –Fibertracking of CST –Spatial interpolation of tract data –Voxel-based analysis of whole brain white matter –Correlation of disease severity with diffusion parameters Quantitative comparison of diffusion parameters between ALS patients and controls –FA –D av

20 DTI - material & methods Subjects –Patients (PA, n = 28) Sex: 14 female, 14 male Age = / years ALS-FRS= /- 6.3 –Controls (CT, n = 26) Age = / years Sex: 15 female, 11 male Imaging (3T) –DTI 16 directions; b= 800 mm²/s; 2mm isotropic resolution –3D-TFE

21 DTI - fibertracking Check integrity of corticospinal tract (CST) –Motor part -> precentral gyrus –Sensory part -> postcentral gyrus  Reconstruct ‘mean’ CST + separate parts  Compare mean FA/D av values between patients and controls

22 DTI - Fibertracking Precentral Postcentral StatsFAp-valueDavp-value MWUFA_mean_L0,0028Dav_mean_L0,0043 FA_mean_R0,0007Dav_mean_R0,0041 StatsFAp-valueD av p-value MWUFA_mean_L0,4150Dav_mean_L0,2273 FA_mean_R0,0016Dav_mean_R0,6211 ** ** * n.s. ** * * StatsFAp-valueD av p-value MWUFA_mean_L0.0041D av _mean_L FA_mean_R<0.0001D av _mean_R0.0243

23 DTI – interpolation of tract data Assess local variation of FA/D av values over course of CST  Interpolation of tract data to spatially ‘normalize’ tract data  Compare mean FA/D av values between patients and controls

24 DTI - Interpolation of tract data Tract data Select part of CST between pons and subcortical WM z Interpolation of individual data in z-direction Measure FA/D av over z-direction of interpolated data $ $ 76 « new » z-coordinates

25 * * * * FA D av

26 DTI – voxel-based analysis Assess WM integrity of whole brain –Normalize FA/D av maps –Smooth warped maps  Voxel-by-voxel comparison of FA/D av values in whole brain

27 DTI – voxel-based analysis t-test X 28 ALS patients X 26 controls X 28 ALS patients X 26 controls Test in each voxel T-value of test FA in PA < CT T-value of test D av in PA > CT

28 DTI – voxel-based analysis CST Orbitofrontal Prefrontal Hippocampal formations Insular regions Parietal regions WM underneath PMC WM underneath SMA p<0.05, FWE corrected

29 DTI - correlation analysis Study effect of patients’ scores on ALS-FRS on FA/D av –ALS-FRS: questionnaire of 12 questions to assess « functional integrity » of patients Questions relate to day-to-day activities max. score = 48 Add individual score as a covariate in a voxel- based correlation analysis

30 A DTI - correlation analysis CST_ALSFRS_FA_positive Frontal_ALSFRS_FA_positive

31 DTI - summary Significant impairment of CST in ALS patients –Limited to the precentral part of the CST –Mostly in cranial parts of the CST White matter impairment is not limited to the motor system –Areas involved in voluntary motor control –Proprioceptive areas –Frontal/temporal/hippocampal structures Strong correlation of ALS-FRS and FA –In CST –Especially in orbitofrontal cortex This study provides support for the view of ALS as being a multisystem degenerative disease, in which abnormalities of extra-motor play an important role in the in vivo physiopathology Sage et al., 2007

32 Other

33 Other Next experiment: Is the cerebral vasculature impaired in ALS? –Basal perfusion -> PWI –Reactivity to respiratory stress conditions -> VASC

34 Study baseline cerebral perfusion –Is a fundamental characteristic of brain tissue Reflects « baseline vascular integrity » –Is needed for « correct » BOLD response –Is altered in numerous pathological processes  Impairment of baseline perfusion could possibly contribute to physiopathology of ALS PWI - introduction

35 This study: use dynamic susceptibility contrast- enhanced T2*-weighted imaging –Administer bolus of paramagnetic contrast agent ° Signal intensity drop at the time of first passage –Signal loss is roughly proportional to log[contrast] –Estimate [contrast] within each voxel and plot it against scan time PWI - materials and methods

36 Use arterial input function (AIF) to deconvolve signal and generate individual quantitative maps –rCBF –rCBV –MTT Use these maps for statistical group comparisons Keston et al, 2003 rCBF rCBV MTT PWI - materials and methods

37 CVR - introduction CVR? « Dynamic autoregulation is a term that describes the moment-to-moment adjustment of arterial vascular resistance to meet the demands of sudden changes in arterial blood pressure » « The physiologic response of vasomotor tone to vasodilatory stress from either functional activation or pharmacologic manipulation is one of the prime features of intact cerebral perfusion »  Fall in perfusion pressure is counterbalanced by vasodilation of cerebral arteries to maintain an adequate CBF Aaslid et al., 1989

38 VASC - materials and methods fMRI –120 dynamics (TE = 33ms; TR = 3000ms; 34 slices; voxel size = 2x2x4mm) –Paradigm: 21s of hyperventilation (HV) alternated with 21s breath-hold (BH) Induce BOLD response (~ ΔSI) without specific task –HV -> ↓ PaCO2 -> vasoconstriction -> ↑ T2*w-signal –BH -> ↑ PaCO2 -> vasodilation -> ↓ T2*w-signal

39 VASC - materials and methods 1 st level analysis: Make VASC map for each subject –Use global intensity changes to ‘model’ the paradigm –Create contrast image containing desired contrast 2 nd level analysis: Make VASC map for groups and make group comparisons –Take contrast images from 1 st level analysis –Create contrast images containing group differences (PAvsCT; CTvsPA)

40 Overview Introduction Aims and methods Results Future directions

41 Future directions - DTI Non-rigid coregistration of DTI data in cooperation with UZ Antwerpen (W. Van Hecke) –To reference –To atlas Tract-based spatial statistics (TBSS, S. Smith et al., 2006)

42 Thank you for your attention Questions?

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