Poster Presentations: P3 P581

amyloidosis and tau pathology, on the frontal lobe, brain metabolism possi-

bly depends on additional factors rather than AD pathology.

P3-084 AVOXEL-BASED MORPHOMETRY COMPARISON

OF THE 3.0TADNI-1 AND ADNI-2 MPRAGE

PROTOCOLS

Simon Brunton1, Cerisse Gunasinghe1, Nigel Jones1, Matthew Kempton2,

Eric Westman3, Andrew Simmons4, 1King’s College London, London,

United Kingdom; 2Kings College London, London, United Kingdom;3Karolinska University, Stockholm, Sweden; 4King’s College London,

London, United Kingdom. Contact e-mail: simon.1.brunton@kcl.ac.uk

Background: The Alzheimer’s Disease Neuroimaging Initiative 3.0T MRI

image acquisition scheme changed between the original ADNI-1 grant and

the two subsequent grants (ADNI-GO and ADNI-2). The aim of the current

study was to investigate the compatibility of the 3.0TADNI-1 and ADNI-2

T1-w volumes using voxel-based morphometry (VBM). Methods: 3D T1-

weightedMPRAGE images of 30 subjects(15-male mean age 32.2 years and

15-female mean age 25.1) were acquired on a 3T GE scanner using the fol-

lowing sequences: ADNI-1: Sagittal 3D-IR-FSPGR, 8-channel coil,

TR¼650ms, TE¼min full, flip-angle¼8 o, slice thickness¼1.2mm, resolu-

tion¼256x256mm, FOV¼26cm. ADNI-2: Sagittal 3D-IR-SPGR, 8-channel

coil, TR¼400ms, TE¼min full, flip-angle¼11 o, slice thickness¼1.2mm,

resolution¼256x256mm, FOV¼26cm. Images were pre-processed and an-

alysed using SPM8. We compared global grey matter (GM), white matter

(WM) and cerebrospinal fluid (CSF), as well as voxel-by-voxel differences

in GM and WM. Results: Correlation coefficients and percentage differ-

ences for each tissue type between ADNI-1 and ADNI-2 were as follows:

((GM R 2 ¼0.78, ADNI-1 4.55% < ADNI-2) (WM R 2 ¼ 0.85, ADNI-1

3.41% > ADNI-2) (CSF R 2 ¼0.81, ADNI-1 0.34% > ADNI-GO)).

ADNI-2: widespread increases in GM most notably in the cerebellum and

pre-central gyrus, and localised decreases along the midline and temporal

lobes. ADNI-1: widespread increases in WM, particularly in the cerebellum

and pre-central gyrus, and localised decreases in the temporal gyrus.

Conclusions: A widespread increase in GM and localised decrease in

WM in ADNI-2 compared to ADNI-1 MPRAGE images suggests that the

image acquisition protocols are not directly comparable. Total volumes of

GM, WM and CSF also differed between the protocols in the following or-

der of magnitude: GM>CSF>WM. This has implications for studies aim-

ing to analyse images acquired using two different protocols using VBM.

P3-085 HIPPOCAMPALTEXTURE PREDICTS

CONVERSION FROM MCI TO ALZHEIMER’S

DISEASE

Lauge Sørensen1, Akshay Pai2, Christian Igel1, Mads Nielsen1, 1University

of Copenhagen, Copenhagen, Denmark; 2Biomediq, Copenhagen,

Denmark. Contact e-mail: madsn@diku.dk

Background: The purpose of this study was to investigate whether baseline

hippocampal MRI texture predicts conversion from MCI to AD after one

year. Methods: A standardized subset of the ADNI database recently re-

leased by ADNI, comprising 169 normal controls (CTRL), 233 MCI, and

101 AD, was considered. The MCIs were further subdivided into AD con-

verters (MCI-C, 41) and non-converts (MCI-NC, 192) after one year. Seg-

mentations of the hippocampi obtained using cross-sectional FreeSurfer

(v5.1.0) were used to define the region of interest (ROI) in each baseline

1.5T T1-weighted MRI scan. A texture-based marker that has demonstrated

good diagnostic capabilities in a previous study was trained to separate

CTRL from AD, and it was subsequently applied to score the MCI-Cs

and the MCI-NCs. The hippocampal fraction (HF) defined as hippocampal

volume divided by intracranial volume (ICV) was also computed based on

the same ROI and on FreeSurfers estimate of ICV. Two markers were

evaluated, hippocampal texture in isolation and a logistic regression model

combining texture, HF, and age. Results were reported by ROC-analysis of

MCI-C vs MCI-NC, testing for significance using DeLong, DeLong and

Clark-Pearson’s test (P<0.05 was regarded as significant). Results: ROC-

curves for prognosis of conversion for the two markers are shown in the

Figure, and the corresponding AUCs were for texture in isolation 0.731

(P<0.001) and for the combined marker 0.754 (P<0.001). Texture, HF,

and age were all significant in the logistic regression model with the follow-

ing P-values: 0.00001, 0.00166, and 0.00011. Conclusions: A novel

texture-based MRI marker was able to predict conversion to AD after one

year in MCI subjects, demonstrating that hippocampal MRI texture at base-

line was related to future cognition. Combining texture with HF and age in-

creased the prognostic accuracy while texture maintained the highest

significance in the combined model. Texture may detect the summarized ef-

fect of several sub-voxel resolution events and may thereby precede struc-

tural changes, making it a promising marker for early detection of AD.

Combining texture with other markers fromMRI relying on structural infor-

mation, such as HF, also has promising perspectives.

P3-086 AMYLOID HUBS IN INDIVIDUAL PiB-PET

IMAGING

Jorge Sepulcre1, John Becker2, Reisa Sperling3, Keith Johnson2, 1Harvard

Medical School, Boston, Massachusetts, United States; 2Massachusetts

General Hospital, Boston, Massachusetts, United States; 3Brigham and

Women’s Hospital, Boston, Massachusetts, United States.

Contact e-mail: sepulcre@nmr.mgh.harvard.edu

Background:Much is known about regional brain atrophy in Alzheim-

er’s disease (AD), yet our knowledge about the network nature of AD-

associated Ab accumulation is limited. In this study, we hypothesized

that PIB binding during individual PET imaging acquisitions may

hold information about temporo-spatial relationships between cerebral

regions. We think that significant association between amyloid accumu-

lations of distributed regions may point out to underlying temporal re-

lationships. For instance, specific regions may predict the amyloid

deposits of other regions in the brain. The aim of this study was to de-

scribe the amyloid hubs that are either affecting or being affected by

other amyloid regions of the brain at the individual level. Methods:

We used PIB-PET images from a cognitive normal sample of elderly

controls (N¼159; age¼74.27) and a Granger causality strategy to study

the forecasting properties of the PIB dynamical signal during individual

acquisitions (Fig. 1-A). Granger causality test is used here for statisti-

cally determining whether time series of PIB signal in brain voxels

are valuable in forecasting another PIB time series of the brain. Differ-

ent lags were used in order to optimize the approach. For each subject,

we computed two connectivity matrices: 1) one that includes all the

Granger out-going associations and 2) another that includes all the

Granger in-coming associations between voxels. Finally, we computed

the degree of connectivity of each voxel in the brain by summing the

out-going or in-coming associations. Results: We identified regions that

accumulate a high number of associations in both, Granger out-going and

Granger in-coming relationships. Amyloid hubs that influence the uptake