Le cerveau vieillissant: apport des études en population

Transcription

1 La substance blanche à travers les âges Le cerveau vieillissant: apport des études en population Bernard Mazoyer Groupe d Imagerie Neurofonctionnelle UMR5296 CNRS CEA Université Bordeaux Segalen

2 TIV Grey matter Normal neuroanatomical aging White matter % TIV Brain loss = 2 cm3 / yr Dekaban, 1978 CSF women men (yrs) Courchesne, 2000

3 Population neuroimaging in aging research Characterization of normal neuroanatomical aging Identification of imaging biomarker of aging Normal values and evolution with age Variance components: genetics, gender, level of education, CV risk factors, HBP, Cohort (longitudinal, follow-up) studies Individual evolution of aging biomarkers Identification of biomarkers of neuropathological events Definition of populations at risk Interventional studies

4 Structural markers of the aging brain Grey matter Reduced global volume Focal/regional atrophy: internal temporal lobe Reduced local tissue density Reduced cortical thickness White matter Reduced global volume Altered microstructure Focal lesions due to SVD CSF Increased global volume Marker evaluation must be: Automated > reproducible Quantitative, global and local Individual > evolution over time

5 MRI tools for assessing of structural markers of the aging brain T1, T2, PD global tissue and target area volumetry whole brain voxel-based morphometry target area morphometry: internal temporal lobe structures, caudate, amygdala, cortical thickness mapping DTI diffusion imaging apparent diffusion coefficient, fractional anisotropy fiber tracking T2, FLAIR, MTR white matter lesions

6 2 examples of white matter population neuroimaging studies White matter loss global white matter atrophy cross-sectional versus longitudinal study of WM rate of atrophy voxel-based identification of fiber tracts most sensitive to aging White matter lesion automated quantification and follow-up common genetic variants associated with WML

7 The EVA and 3 Cities population neuroimaging study in elderly N Age range MRI Ψtests DNA EVA 845 [63, 75] 3C-Dijon1 1,924 [65, 82] 3D-T1 (0.9 mm) T2/PD (5 mm) (T+4) MMSE, TMTA/B, Rey15, Benton FRT, Wechsler, BentonVRT, FTT, Raven, PASAT, LET (T0,T+2,T4,T+6,T+7,T+9) YES 3C-Dijon2 1,442 [69, 86] 3C-Bordeaux1 657 [65, 80] 3D-T1 (1mm) T2/PD (3mm) (T0, T+4) MMSE, TMTA/B, Rey5, BentonVRT, Isaacs set, NART (T0,T+2,T+4, T+10) GWAS (540K) 3C-Bordeaux2 433 [69, 84] Highly homogeneous «large» cohorts and MRI datasets Capacity of cross-sectional and longitudinal studies Genome wide genotype available (3C)

8 1. White matter loss in the elderly

9 Tissue voxel-based morphometry IRMT1 T1 IRM T2 SB SG LCR GM WM CSF individual tissue segmentation spatial normalisation and averaging (N=750) Volumetry (ʃ V ) GMvol WMvol CSFvol TIV

10 Age and sex effects on the neuroanatomy of healthy elderly Cross-sectional approach (EVA) Tissue Men slope (cc / yr) Women GM -1.73* -2.67*** WM -1.67* -1.62* CSF 3.34*** 4.01*** GM / WM ns ns no Sex by Age interaction * < 0.05, ** < 0.01, *** < Lemaître et al, Neuroimage, 2005

11 Age and sex effects on the neuroanatomy of healthy elderly Longitudinal approach (3C-Dijon) Individual annual rate of brain tissue volume variation Mean (cc/yr) Sex (M vs F) (cc /yr) Age (cc/yr/yr) Sex by Age (cc/yr/yr) HBP (cc/yr) GM -4.1 *** *** WM -1.2 *** *** CSF +4.7 *** *** < Brain tissue is lost at constant rate

12 Areas of maximal annual rate of white matter loss* T value p<0.05 corr. *adjusted for WM total volume GIN, UMR5296

13 Areas of larger annual rate of white matter loss* in men %/yr p < 0.05 corr *adjusted for TIV GIN, UMR5296

14 Areas with increasing annual rate of tissue loss or gain GM WM %/yr/yr %/yr/yr CSF %/yr/yr GIN, UMR5296

15 2. White matter lesion in the elderly

16 White matter lesions (WML) MRI signal hyperintensities on T2/FLAIR (e) 1. Dilation of Virchow-Robin spaces (d) 2. Small vessel diseases (f) Presence in healthy controls Linked during aging to: cognitive decline gait troubles depression second episode of cerebral infarct AD Schmidt, Acta Neuropathol 2011 Biological marker for Risk factors of neuropathologies Clinical trials Automatic detection mandatory

17 White matter lesions longitudinal evaluation (WHALE ) baseline +4 years WML Total WML retrieved WML retrievedlost Juxta-WML Peri-WML Deep-WML retrieved / lost /new Age 0.026*** 0.025*** ns 0.015*** ** ns Sex ns ns ns ns ns ns HBP ns ns ns ns ns ns Sex x HBP 0.013** 0.010** * * 0.007* ns Maillard et al, Neuradiol 2009, 50:

18 Rationale for searching genetic risk factors for WML WML volume is highly inheritable (55-80%) Silent infarcts are subject to a familial risk factor Intermediary phenotypes, such as WML, expected to be more influenced by genetic variants than complex outcome phenotypes such as stroke or dementia Genetic risk factor population studies are easy and non-invasive

19 Genome-wide association studies (GWAS) SNP: 0,1% of genetic variation DNA chip: test up to SNP

20 Genome-wide association studies of white matter lesion burden: the CHARGE consortium SCI

21 GWAS du volume d HSB 8 5 x 10-8 rs (MAF=0.18), p=4x10-9, TRIM65 17q

22 Association du locus 17q25 avec vol HSB rs p = 4x10-9 rs p = 4x10-8 UNC13D WBP2 TRIM47 MRPL38 TRIM65 FBF1 ACOX1 ring-finger B-box coiled-coil proteins: immunity, apoptosis, neuroprotection

23 CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology) 7 cohorts, 9,361 subjects, 69.5 years on average Rotterdam Study I, II Cardiovascular Health Study (CHS) Framingham Heart Study (FHS) Atherosclerosis Risk in Communities Study (ARIC) Age Gene-Environment Susceptibility / Reykjavik Study (AGES) Austrian Stroke Prevention Study (ASPS) + Closely Collaborating Studies: Three-Cities (3C) Study, TASCOG etc Psaty et al., Circ Cardiovasc Genet 2009