Florent Roche

ACCURACY OF BMAS HIPPOCAMPUS SEGMENTATION USING THE HARMONIZED HIPPOCAMPAL PROTOCOL

 Hippocampal volume (HCV) measured with MRI has been widely used as a key biomarker for both improving subject selection and monitoring treatment efficacy in Alzheimer’s Disease (AD) studies. However various hippocampal protocols exist in the literature, each including a different set of subfields and sub-structures, potentially leading to confusion and additional complexity for direct comparison and consistency in reporting study results.

Reproducibility of intracranial and hippocampal volume quantification at 1.5T and 3T MRI: Application to ADNI I

in metabolite levels of the phosphomonoesters phosphocholine (PCho) and phosphoethanolamine (PEtn), and of the phosphodiesters glycerophosphocholine (GPCho) and glycerophosphoethanolamine (GPEtn), inorganic phosphate, phosphocreatine and ATP. Results: High quality 31 P-MRSI spectra with well resolved resonance for phospholipids were obtained (see Figure). Relative levels of PCho, PEtn, GPCho and GPEtn to total phosphorus signal and the ratio of phosphomonoesters to phosphodiesters will be presented for medial temporal lobe, medial prefrontal cortex and posterior cingulate cortex. It is anticipated that data will be presented from a total number of approximately 20 patients. Conclusions: This clinical study will be the first to provide information on brain phospholipid metabolism in multiple brain regions in patients with Alzheimer’s disease. Souvenaid is a registered trademark of N.V. Nutricia.

LONGITUDINAL EFFECTS OF PARTIAL VOLUME CHANGES ON SUVR VALUES

Figure 2. Posterior prediction for the individual time shift in the testing data. Healthy individuals are generally displaced at the early stages of the pathology, while the predictions for MCI and AD patients are associated with respectively intermediate and late progression stages. NL: normal individuals, MCI: mild cognitive impairment, AD: Alzheimer’s patients. Healthy individuals are associated with the early stages of the pathology in both training and testing data, while MCI and AD are characterized by late stages (p<1e-4 for the group-wise comparison). The temporal cut-off based on the 10th quantile of the staging in the training AD population (t1⁄43.6 years) leads to a discrimination accuracy of 0.84 for MCI converters vs stable in the testing data.

ASSESSING REGIONAL CORTICAL THICKNESS FOR PREDICTING MCI CONVERSION TO AD

METHODS MRI data Cortical thickness measurements were performed on the Baseline 3DT1-weighted sequences of 232 MCI subjects (116 who did not convert to AD within 36 months, 116 who did) from the ADNI-1 database (http://adni.loni.ucla.edu). Image processing FreeSurfer v5.3 (http://surfer.nmr.mgh.harvard.edu/) [1, 2] was used for the determination of the white/gray boundary (in blue below), pial/CSF boundary (in green below) and subcortical parcellation (see Fig. 1). Cortical thickness was assessed by solving Laplace’s equation [3], building the set of paths between the inner and outer cortical surfaces and deriving the average thickness from them, for each subcortical region provided by FreeSurfer, as well as overall.

Quantifying neocortical structural changes for clinical trials in Alzheimer’s disease: Comparison between tensor-based morphometry and longitudinal freesurfer

value ratio (SUVRs) 1.1, measured from a standard space template consisting of 6 cortical regions. For comparison to a wholecerebellar reference region, a second normalization was performed on the longitudinal data using a centrum semiovale region as a correction factor (Figure). This method has been shown to improve signal to noise, while preserving the ability to use cerebellar SUVRs at baseline. Analysis-of-Covariance models adjusted by baseline, study, treatment and age were used to assess baselineto-endpoint change between treatment and placebo groups. For sample size estimations, 80% power and a1⁄40.05 were used to detect magnitude of observed 18 month changes from baseline in the placebo group. Results:Using a whole-cerebellar reference region at baseline and endpoint, least squares mean SUVRs for the placebo group increased 0.00460.0129 (0.49%60.91), and for the active treatment group decreased 0.00660.0137 (0.19%6 0.96)(p1⁄40.62). Power analysis revealed a sample size of n1⁄44056 to detect a difference between 18 month placebo group change and baseline SUVRs (i.e. no change in treatment group from baseline). White matter adjustments resulted in a mean increase of 0.01160.0075 (0.79%60.54) in the placebo group and mean decrease of 0.00860.008 (-0.6%60.57) in the active treatment group (p1⁄40.08); the calculated sample size fell to n1⁄4421. Conclusions:Adjusting longitudinal SUVRs with a white matter reference region in these phase 3 anti-amyloid treatment trials increased mean change detection and decreased variance. This method resulted in a substantial improvement in statistical power to detect change. Reference: Abhinay Joshi, Michael Pontecorvo, Michael A. Navitsky, Ian A. Kennedy, Mark Mintun, Michael D. Devous. Measuring change in beta–amylod burden over time using florbetapir-PET and a subcortical white matter reference region. Alzheimer’s Dement. 2014;10(4):902.

A jacobian-based method to assess changes in cortical thickness: Application to ADNI data and comparison with longitudinal freesurfer

ERC and ADAS13 scores were 35%, 30% and 7%. Thirty-eight (38) CN subjects progressed to incident MCI, but neither age nor education predicted progression. In univariate Cox models, ADAS13 score (c1⁄414.2, p<.0001) and ERC normalized volume (c1⁄46.2, p1⁄40.01) predicted progression, while amyloid SUVR showed a trend toward significant prediction (c1⁄43.2, p1⁄4.07). In multivariate Cox regression, only ADAS13 scores predicted progression (c 1⁄48.3, p1⁄4.004). Using dichotomized variables, all predictors were significant in univariate analyses, but none provided unique variance in multivariate models. The estimated 2.1 year progression rates to MCI and 95% CIs were:

Microhemorrhage Findings on Baseline Rosas Mri Data

a potential approach for the treatment of AD and a BACE radioligand could be valuable for assessing target occupancy and therapeutic responses to BACE inhibition. Methods: Mdr1a/b (P-gp 3 and 1 deficient, termed P-gp KO), FVB wild type (wt), BACE1 knockout (KO), and BACE1 wt mice, were purchased fromTaconic.Mdr1a/b andFVBWTmicewere 8weeks old,whileBACE1KO and BACE1 WT mice were two years old when used in the studies. The unlabeled compounds and [3 H]Compound-A (75.3Ci/mmol) were synthesized in house. Autoradiography was performed using frozen brain slices. In vitro binding assays were done with brain membranes or homogenates. In vivo brain occupancy was performed in Mdr1a/b, FVB and BACE1 KO mice by i.p. administration of BACE inhibitor or vehicle and then followed by i.v. dosing of the radioligand. Results: In mouse brain homogenates, [3 H]Compound-A showed binding site densities (Bmax) from 6.5 nM to 8.4 nM and high binding affinity (K d1⁄4 0.5 nM), yielding good binding potentials (Bmax/K d). In autoradiography of BACE1 wt mice, [3 H]Compound-A binding sites were widely distributed across the brain with different binding densities in various regions, and the non-displaceable bindingwas low and homogenous among all the brain slices. In contrast, [3 H]Compound-A failed to show specific binding in the brain slices of BACE1 KO mice. Furthermore, the in vivo brain occupancy studies demonstrated that [3 H]Compound-A binding to Mdr1a/b brains was dose-dependent and displaceable by pre-administration of a selective, brain penetrant BACE inhibitor. In FVB mouse brains, [3 H]Compound-A did not show specific brain uptake following i.v. administration of [3 H]CompoundA. Conclusions: These results demonstrate that [3 H]Compound-A binding is specific to BACE which is widely distributed in mouse brain. In vivo brain uptake of [3 H]-Compound-A only occurred in P-gp KO mice, not in wild type and BACE1 KO mice. Together, these data suggest that a compound with no P-gp liability could be developed into a PET tracer for BACE.

Impact of parallel imaging on volumetric brain MRI measurements in ADNI-GO and ADNI-2

Regression analysis of the hyperintensity accumulation (EV) scores from two of our methods (RC and RR) with six vascular factors, CL Cholesterol, HDL High density Lipoprotein, SBP Systolic Blood Pressure, DBP Diastolic Blood Pressure, BMI Body Mass Index and CV Risk 10–year cardiovascular risk: EV –RC (EV –RR resp.) denotes the EV s calculated using RC (RR resp.) method. Significant p-values (and corresponding t– values) at 95% confidence level and higher are bold-faced.