6 Douglas Mental Health University Institute, Montreal, QC, Canada; 7 McConnell Brain Imaging Centre - McGill University, Montr eal, QC, Canada; 8 McGill University Research Centre for Studies in Aging, Verdun, QC, Canada; 9 Translational Neuroimaging Laboratory, Verdun, QC, Canada; 10 Translational Neuroimaging Laboratory-McGill University, Verdun, QC, Canada; 11 Douglas Mental Health Institute, Montreal, QC, Canada; 12 McGill Center for Research in Aging, Montreal, QC, Canada; 13 Cerebral Imaging Centre-Douglas Research Centre, Verdun, QC, Canada; 14 McGill Center for Studies in Aging, Montreal, QC, Canada; 15 Centre for Studies on Prevention of Alzheimer’s Disease (StoP-AD Centre), Douglas Mental Health Institute, Verdun, QC, Canada; 16 Translational Neuroimaging Laboratory, McGill University, Montreal, QC, Canada. Contact e-mail: mni.qureshi@mail.mcgill.ca Background: The brain functional decline is a Hallmark of Alz- heimer’s disease (AD). Here, we examine the effect of Tau and Am- yloid networks on whole-brain functional connectivity. We hypothesized that the protein aggregates deposition will suppress the strength of connectivity in MCI and AD. Methods: We studied CN (n¼90), mild cognitive impairment MCI (n¼22), and AD (n¼36) subjects of the transnational bio-marker aging and demen- tia cohort (TRIAD;McGill University, Canada). A global (whole brain gray matter voxels) correlation technique is used to measure the associations between protein deposition and functional connec- tivity across brain voxels. All the PET images from each study group were processed in the following steps. For T1 structural MRI images, we applied N4 bias correction, brain masking (extrac- tion), tissue segmentation, and template registration. For PET im- ages, we applied smoothing by c3d, T1 registration, template registration, and created SUVR images by ImageMath function of ANTs tool. Later these SUVR image volumes were concatenated in a time-series fashion to obtain the global connectivity maps. An AFNI program 3dTcorrMap was used for this purpose on PET im- ages. It computes the voxel-wise correlation from each non-zero voxel as a seed to all the non-zero voxels in the brain and creates the connectivity maps. Multiband rs-fMRI data was processed us- ing AFNI with non-linear structural MRI registration from Freesur- fer software. An AFNI program, 3dNetCorr was used for generating the connectivity maps. Later these maps were z-trans- formed, then all these connectivity maps were averaged together to create a global average connectivity map. Finally, we created subtraction images from the final global correlation images to visu- alize the difference between each sub-group for the same protein aggregates and functional connectivity of the rs-fMRI. Results: We found that the increased Amyloid and Tau networks leads to a to the reduction of the rs-functional connectivity strength. These findings are observed at group-level network analysis. Conclusions: This preliminary analysis that rs-functional degradation secondary to protein aggregation occurs globally and is not segregated to specific brain networks. P2-347 OVERLAP PATTERN OF HYPERMETABOLISM AND BRAIN ATROPHY RELATED WITH OBESITY IN HEALTHY ELDERLY Jordi Pegueroles 1,2 , Adriana Pane 3 , Victor Montal 1,2 , Eduard Vilaplana 1,2 , Rafael Blesa 1,2 , Alberto Lle o 1,2 , Amanda Jimenez 3,4 , Juan Fortea 1,2 , 1 Centre of Biomedical Investigation Network for Neurodegenerative Diseases, Madrid, Spain; 2 IIB-Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain; 3 Obesity Unit, Department of Endocrinology and Nutrition, Hospital Cl  ınic, Barcelona, Barcelona, Spain; 4 Institut d’Investigacions Biomediques August Pi Sunyer, Barcelona, Spain. Contact e-mail: jpegueroles@santpau.cat Background: Mid-life obesity is a well-established risk factor for Alzheimer’s Disease (AD). However the impact of late-life obesity on brain structure and metabolism and whether it has a differential effect in males and females is under debate. Therefore, the purpose of this work is to assess the role of late-life obesity and its impact on brain metabolism and structure in healthy elderly and disentangle a potential differential effect of sex in this association. Methods: We included all healthy elderly subjects from the ADNI2 database (http://adni.loni.usc.edu/) with a 3T-MRI and FDG-PET available. Body mass index (BMI) was used as a measure of obesity. The cortical thickness (CTh) reconstruction of structural MRIs was per- formed with Freesurfer (https://surfer.nmr.mgh.harvard.edu/, version 6). PET-FDG was analyzed following a surface-based approach, taking advantage of the Freesurfer segmentation. Briefly, images were registered to the T1, intensity-scaled by the pons-ver- mis region and then projected to the brain surface for further analyzing. We peformed BMI correlation analyses for both CTh and FDG in the whole sample and stratyfing by sex. Age, sex, ed- ucation level, triglycerides and cholesterol were used as covariates. All the analyses were corrected by multiple-comparisons (FWE p<0.05). Results: The final sample was composed of 155 individ- uals (79 females), with a mean age of 73.7666.4 and mean BMI of 27.2264.0 kg/m2. When analyzing the whole sample (figure1), BMI was related to a widespread increased metabolism in temporal, parietal and frontal areas and to decreased CTh, with a high degree of overlap between measures. We then stratyfied by sex (figure 2). Poster Presentations: Monday, July 15, 2019 P728