- Email: [email protected]
MORPHOMETRIC CORTICAL NORMATIVE DATA THROUGHOUT AGING
Poster Presentations: Tuesday, July 26, 2016
atrophy. Models investigating the association between vertex-wise AV1451 and thinning controlled for baseline thickness, age, sex, and PIB-PET as a measure of amyloid burden. Additional models used AV1451 ROIs from regions showing significant relationships in whole-brain maps, and included the same covariates as wholebrain analyses. Results: Whole-brain maps relating AV1451 signal and ROI slope at a threshold of p<0.05, uncorrected, showed widespread negative associations between parahippocampal thinning and AV1451, especially in temporal regions. ROI analyses showed significant relationships between parahippocampal thinning and AV1451 in bilateral inferior temporal (left: t(92)¼-2.60,p¼0.011; right: t(92)¼-2.72,p¼0.0077) and middle temporal cortex (left: t(92)¼-3.65,p¼0.00043; right: t(92)¼-3.27,p¼0.0015), and left entorhinal (t(92)¼-2.73,p¼0.0076), isthmus cingulate (t(92)¼3.34,p¼0.0012), and posterior cingulate cortex (t(92)¼2.57,p¼0.012). Entorhinal thinning was associated with temporal pole AV1451 (left: t(92)¼-3.07,p¼0.0028, right: t(92)¼3.27,p¼0.0015). Maps of AV1451 also showed relations between posterior cingulate thinning and precuneus AV1451 (left: t(92)¼2.67, p¼0.0090, right: (92)¼3.33,p¼0.0012). Conclusions: Parahippocampal cortex thinning over a prior three-year period predicts a widespread pattern of increased tau pathology, especially in the temporal lobe. Entorhinal and posterior cingulate thinning predicted increased temporal and parietal AV1451, respectively, but the maps were more localized. This is consistent with autopsy studies, which show tau pathology and neurodegeneration largely restricted to the temporal lobe in cognitively normal elderly individuals. Further study is ongoing to determine the relationship between AV1451 and prospective changes in cortical thickness.
P3-277
MORPHOMETRIC CORTICAL NORMATIVE DATA THROUGHOUT AGING
Olivier Potvin1, Abderazzak Mouiha1, Louis Dieumegarde1, Simon Duchesne1,2, 1Institut Universitaire en Sante Mentale de Quebec, Quebec, QC, Canada; 2Departement de Radiologie et Medecine Nucleaire, Universite Laval, Quebec, QC, Canada. Contact e-mail: Olivier.Potvin.1@ ulaval.ca
P941
Background: Structural neuroimaging data from cognitively healthy individuals across the lifespan are necessary if one is to assess deviation from normality. Unfortunately, such normative data are found lacking, especially in such a form as to be widely applicable, allowing cross-studies comparisons. Our objective was to produce normative values for regional cortical surfaces, thicknesses and volumes using a large sample of cognitively healthy adults and a free, widely available segmentation technique. Methods: We merged 3D T1-weighted MRI scans from 2,194 healthy adults (1,078 women; 49.1%) aged 18 to 94 years old (mean: 49.6; SD: 20.9). Data originated from fifteen datasets (Table 1). We used FreeSurfer (Version 5.3) to extract cortical surfaces, thicknesses, and volumes based on the “Desikan–Killiany–Tourville” (DKT) cortical atlas containing 31 bilateral subdivisions. We generated predictive models for each region with age, sex, intracranial volume (TIV), magnetic field strength (MFS), and manufacturer as predictors. Predictors’ selection was based on the predicted residual sum of squares statistic using a 10-fold cross-validation. Results: The mean R2 of the 186 cortical measures revealed that the predictors explained a substantial amount of variance (.40). The mean R2 of each predictor showed that age (.17), sex (.08), and TIV (.12) explained most of the variance while MFS (.01), manufacturer (.02) and interactions between predictors (.01) explained only a limited amount. We report results for both hemisphere of the cortex as examples (formulas are included in Table 2). R2 for surface (L: .76, R: .77), thickness (L: .46, R: .44), and volumes (L: .80, R: .80) were considerably larger than that for cortical regional models. The strongest effects were TIV (L: .35, R: .35) and sex (R2: L: .24, R: .25) for surface, age (R2: L: .40, R: .39) and MFS (L: .02, R: 02) for thickness, and age (L: .38, R: .38) and TIV (L:
P942
Poster Presentations: Tuesday, July 26, 2016
Database
Funding/Acknowlegments
Autism Brain Imaging Data Exchange (ABIDE)
Primary support for the work by Adriana Di Martino was provided by the NIMH (K23MH087770) and the Leon Levy Foundation. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to MPM (R03MH096321). Funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Company, Medpace, Inc, Merck and Co, Inc, Novartis, Pfizer Inc, F. Hoffman-La Roche, Schering- Plough, Synarc, Inc, and Wyeth, as well as nonprofit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by National Institutes of Health P30 AG010129, KOI AG030514, and the Dana Foundation. Part of the data used in this abstract was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). See www.aibl.csiro.au for further details. The imaging data and phenotypic information was collected and shared by the Mind Research Network and the University of New Mexico funded by a National Institute of Health COBRE: 1P20RR021938-01A2. Funded by the National Multiple Sclerosis Society. Provided by the Biomedical Informatics Research Network under the following support: U24RR021992. Data collected as part of the project: EPSRC GR/S21533/02 - http://www.brain-development.org/ Landman, B.A. et al. “Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study”, Neurolmage. (2010) NIHMS/PMC:252138 http://dx.doi.org/10.1016/j.neuroimage.2010.11. 047 (http://miriad.drc.ion.ucl.ac.uk). The MIRIAD investigators did not participate in analysis or writing of this report. The MIRIAD dataset is made available through the support of the UK Alzheimer’s Society (RF116). The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline (6GKC). Principal support for the enhanced NKI-RS project is provided by the NIMH BRAINS R01MH094639- 01. Funding for key personnel also provided in part by the New York State Office of Mental Health and Research Foundation for Mental Hygiene. Funding for the decompression and augmentation of administrative and phenotypic protocols provided by a grant from the Child Mind Institute (1FDN2012-1). Additional personnel support provided by the Center for the Developing Brain at the Child Mind Institute, as well as NIMH R01MH081218, R01MH083246, and R21MH084126. Project support also provided by the NKI Center for Advanced Brain Imaging (CABI), the Brain Research Foundation, the Stavros Niarchos Foundation and the NIH P50 MH086385-S1 (phase 1). The OASIS project was funded by grants P50 AG05681, P01AG03991, R01 AG021910, P50 MH071616, U24 RR021382, and R01 MH56584. This databse was supported by NIH R21NS061144 R01NS32979 R01HD057076 U54MH091657 K23DC006638 P50 MH71616 P60 DK020579-31, McDonnell Foundation Collaborative Action Award, NSF IGERT DGE-0548890, Simon’s Foundation Autism Research Initiative grant, Burroughs Wellcome Fund, Charles A. Dana Foundation, Brooks Family Fund, Tourette Syndrome Association, Barnes- Jewish Hospital Foundation, McDonnell Center for Systems Neuroscience, Alvin J. Siteman Cancer Center, American Hearing Research Foundation grant, Diabetes Research and Training Center at Washington University grant. www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen Idee, Bristol-Myers, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, and UCB. Data collected at the Biomedical Imaging Center at the Beckman Institute for Advanced Science and Technology at UIUC. Funded by the Office of Naval Research (ONR): N00014-07-1-0903.
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) Center of Biomedical Research Excellence (COBRE) Cleveland Clinic (Cleveland CCF) Functional Biomedical Informatics Research Network (FBIRN) Information extraction from Images (1X1) KIRBY21
Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD)
Nathan Kline Institute Rockland (NKI-R) sample (phase 1) and NKI-R Enhanced sample (phase 2)
Open access series of imaging studies (OASIS) Power
Parkinson’s Progression Markers Initiative (PPMI)
TRAIN-39
-6.88E-03 -1.33E+02 13223.7
P943
.23, R: .23) for volume. Figure 1 displays age and sex effects on each measure. Conclusions: Formulas derived from these models can be used by other researchers using similar techniques to predict expected surfaces/thicknesses/volumes for new individuals and measure deviations from the normality, according to the study’s characteristics.
P3-278
PROTECTIVE MECHANISMS OF COGNITIVE RESERVE REVEALED BY MULTIMODAL NEUROIMAGING MARKERS
Hwamee Oh1, Qolamreza Razlighi1, Yunglin Gazes1, Christian Habeck1, Yaakov Stern2, 1Columbia University, New York, NY, USA; 2Columbia University College of Physicians and Surgeons, New York, NY, USA. Contact e-mail: [email protected] Background: Several individual differences factors, which are
Int: Intercept. MFS: Magnetic field strength. SD: Standard deviation based on the square root of the mean square error. Sex: men ¼ 1, women ¼ 0. MFS: l.5T ¼ 1, 3T ¼ 0. Manufacturer: GE ¼ 1, Philips ¼ 1, Siemens ¼ 0. Age and TIV are centered by the mean (age - 49.6, TIV -1527850.6).
-4.79E-14 -6.10E+03 -1.50E+04 -1.15E+04 7.44E+03 6.53E+03
5.09E-06 -1.22E-06 -2.87E-02 1.47E-08 -7.15E-14 2.96E-19 -6.14E-02 3.68E-02 -3.64E-02 -6.77E-02 7.24E-02 1.95E+00 -1.73E-01 2.23E+03 1.07E-01 2.47E-08 -5.64E-14 -5.87E+03 -1.72E+04 -1.16E+04 1.05E+04 6.66E+03
2.52E+01 -5.76E+03 -1.86E+03 5.02E+03 -5.76E+02 -5.63E+01 -6.27E+03 -2.14E+03 5.10E+03 -1.24E+02 -6.76E-02 2.16E-02 -4.57E-02 -5.45E-02 8.84E-02 -9.13E-01 -4.20E-02 1.54E+03 3.99E-02 4.33E-09 -9.33E-01 -3.38E-02 1.71E+03 3.98E-02 5.79E-09 7.47E-06 -1.19E-06 -3.01E-02 5.24E-08 -6.74E-14
Left surface 83047.2 -7.98E+01 Right surface 83514.1 -8.54E+01 Left 2.4 -2.52E-03 thickness Right thickness 2.4 -2.42E-03 Left 224167.0 -5.07E+02 volume Right volume 225465.9 -5.09E+02
1.69E+00 -1.65E-01 2.44E+03 1.10E-01 2.88E-08
4671.3 4649.4 0.1 -2.94E+01 -2.87E+01 -4.86E-04
-4.42E-04 0.1 -1.50E+02 13306.9
SD Age Int Cortical measure
Coefficients
Age2
Age3
Sex
TIV
TIV2
TIV3
MFS
GE
Philips
GE X MFS
Philips X MFS
TIV X MFS
Age X Sex
Poster Presentations: Tuesday, July 26, 2016
collectively termed as “reserve”, have been proposed to protect older adults from aging and age-related neurodegenerative diseases. Neural mechanisms underlying the protective pathways mediated by reserve, however, remain to be elucidated. In this study, we examined an association between cognitive reserve (CR) and multimodal neuroimaging markers including beta-amyloid plaques, gray matter (GM) volume, and task-related brain activation to probe neural substrates instantiating reserve. Methods: Forty-six young (mean age ¼ 26, SD ¼ 3) and 65 cognitively normal older adults (mean age ¼ 64.6,SD ¼ 3) underwent two functional magnetic resonance imaging (fMRI) sessions. During fMRI sessions, subjects performed verbal working memory and cognitive control tasks using a letter Sternberg task with varying load (1, 3, or 6 letters) and task-switching (for a single task condition: either vowel/consonant or lower/upper case judgment; for a dual task condition: both tasks), respectively. All older subjects additionally completed 18F-Florbetaben PET and a global amyloid index was computed by an averaged cortical standardized uptake value ratio (SUVR) using a gray matter cerebellum reference region. For each subject, T1-weighted high-resolution structural images were processed using voxel-based morphometry to assess GM volume in a whole-brain template space. A composite score of vocabulary test, American National Adult Reading Test (AMNART), and years of education served as a proxy of CR for each individual. Results: With higher CR, older subjects showed lower amyloid deposition in temporoparietal cortex, parahippocampal gyrus, hippocampus, and left frontal cortex. For wholebrain gray matter volume, higher CR was associated with larger GM volume in lateral occipitotemporal cortex, parahippocampal gyrus, lateral parietal cortex, posterior cingulate, precuneus, and premotor cortex. For task-related activation from both tasks, older adults with higher CR showed less increases in activation as task difficulty increases. Conclusions: The present results suggest that CR may exert its protective role via multiple neural pathways through which the aging brain is protected from accumulation of beta-amyloid and gray matter atrophy, while recruiting brain activation in a more efficient manner. Future studies with longitudinal evaluation will be needed for a more direct assessment of causality.
P3-279
REDUCED SUPRAMARGINAL GYRUS GRAY MATTER VOLUME ASSOCIATED WITH COGNITIVE IMPAIRMENT IN ALZHEIMER’S DISEASE: A 7-TESLA MRI STUDY
atrophy. Models investigating the association between vertex-wise AV1451 and thinning controlled for baseline thickness, age, sex, and PIB-PET as a measure of amyloid burden. Additional models used AV1451 ROIs from regions showing significant relationships in whole-brain maps, and included the same covariates as wholebrain analyses. Results: Whole-brain maps relating AV1451 signal and ROI slope at a threshold of p<0.05, uncorrected, showed widespread negative associations between parahippocampal thinning and AV1451, especially in temporal regions. ROI analyses showed significant relationships between parahippocampal thinning and AV1451 in bilateral inferior temporal (left: t(92)¼-2.60,p¼0.011; right: t(92)¼-2.72,p¼0.0077) and middle temporal cortex (left: t(92)¼-3.65,p¼0.00043; right: t(92)¼-3.27,p¼0.0015), and left entorhinal (t(92)¼-2.73,p¼0.0076), isthmus cingulate (t(92)¼3.34,p¼0.0012), and posterior cingulate cortex (t(92)¼2.57,p¼0.012). Entorhinal thinning was associated with temporal pole AV1451 (left: t(92)¼-3.07,p¼0.0028, right: t(92)¼3.27,p¼0.0015). Maps of AV1451 also showed relations between posterior cingulate thinning and precuneus AV1451 (left: t(92)¼2.67, p¼0.0090, right: (92)¼3.33,p¼0.0012). Conclusions: Parahippocampal cortex thinning over a prior three-year period predicts a widespread pattern of increased tau pathology, especially in the temporal lobe. Entorhinal and posterior cingulate thinning predicted increased temporal and parietal AV1451, respectively, but the maps were more localized. This is consistent with autopsy studies, which show tau pathology and neurodegeneration largely restricted to the temporal lobe in cognitively normal elderly individuals. Further study is ongoing to determine the relationship between AV1451 and prospective changes in cortical thickness.
P3-277
MORPHOMETRIC CORTICAL NORMATIVE DATA THROUGHOUT AGING
Olivier Potvin1, Abderazzak Mouiha1, Louis Dieumegarde1, Simon Duchesne1,2, 1Institut Universitaire en Sante Mentale de Quebec, Quebec, QC, Canada; 2Departement de Radiologie et Medecine Nucleaire, Universite Laval, Quebec, QC, Canada. Contact e-mail: Olivier.Potvin.1@ ulaval.ca
P941
Background: Structural neuroimaging data from cognitively healthy individuals across the lifespan are necessary if one is to assess deviation from normality. Unfortunately, such normative data are found lacking, especially in such a form as to be widely applicable, allowing cross-studies comparisons. Our objective was to produce normative values for regional cortical surfaces, thicknesses and volumes using a large sample of cognitively healthy adults and a free, widely available segmentation technique. Methods: We merged 3D T1-weighted MRI scans from 2,194 healthy adults (1,078 women; 49.1%) aged 18 to 94 years old (mean: 49.6; SD: 20.9). Data originated from fifteen datasets (Table 1). We used FreeSurfer (Version 5.3) to extract cortical surfaces, thicknesses, and volumes based on the “Desikan–Killiany–Tourville” (DKT) cortical atlas containing 31 bilateral subdivisions. We generated predictive models for each region with age, sex, intracranial volume (TIV), magnetic field strength (MFS), and manufacturer as predictors. Predictors’ selection was based on the predicted residual sum of squares statistic using a 10-fold cross-validation. Results: The mean R2 of the 186 cortical measures revealed that the predictors explained a substantial amount of variance (.40). The mean R2 of each predictor showed that age (.17), sex (.08), and TIV (.12) explained most of the variance while MFS (.01), manufacturer (.02) and interactions between predictors (.01) explained only a limited amount. We report results for both hemisphere of the cortex as examples (formulas are included in Table 2). R2 for surface (L: .76, R: .77), thickness (L: .46, R: .44), and volumes (L: .80, R: .80) were considerably larger than that for cortical regional models. The strongest effects were TIV (L: .35, R: .35) and sex (R2: L: .24, R: .25) for surface, age (R2: L: .40, R: .39) and MFS (L: .02, R: 02) for thickness, and age (L: .38, R: .38) and TIV (L:
P942
Poster Presentations: Tuesday, July 26, 2016
Database
Funding/Acknowlegments
Autism Brain Imaging Data Exchange (ABIDE)
Primary support for the work by Adriana Di Martino was provided by the NIMH (K23MH087770) and the Leon Levy Foundation. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to MPM (R03MH096321). Funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Company, Medpace, Inc, Merck and Co, Inc, Novartis, Pfizer Inc, F. Hoffman-La Roche, Schering- Plough, Synarc, Inc, and Wyeth, as well as nonprofit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org/). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by National Institutes of Health P30 AG010129, KOI AG030514, and the Dana Foundation. Part of the data used in this abstract was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). See www.aibl.csiro.au for further details. The imaging data and phenotypic information was collected and shared by the Mind Research Network and the University of New Mexico funded by a National Institute of Health COBRE: 1P20RR021938-01A2. Funded by the National Multiple Sclerosis Society. Provided by the Biomedical Informatics Research Network under the following support: U24RR021992. Data collected as part of the project: EPSRC GR/S21533/02 - http://www.brain-development.org/ Landman, B.A. et al. “Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study”, Neurolmage. (2010) NIHMS/PMC:252138 http://dx.doi.org/10.1016/j.neuroimage.2010.11. 047 (http://miriad.drc.ion.ucl.ac.uk). The MIRIAD investigators did not participate in analysis or writing of this report. The MIRIAD dataset is made available through the support of the UK Alzheimer’s Society (RF116). The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline (6GKC). Principal support for the enhanced NKI-RS project is provided by the NIMH BRAINS R01MH094639- 01. Funding for key personnel also provided in part by the New York State Office of Mental Health and Research Foundation for Mental Hygiene. Funding for the decompression and augmentation of administrative and phenotypic protocols provided by a grant from the Child Mind Institute (1FDN2012-1). Additional personnel support provided by the Center for the Developing Brain at the Child Mind Institute, as well as NIMH R01MH081218, R01MH083246, and R21MH084126. Project support also provided by the NKI Center for Advanced Brain Imaging (CABI), the Brain Research Foundation, the Stavros Niarchos Foundation and the NIH P50 MH086385-S1 (phase 1). The OASIS project was funded by grants P50 AG05681, P01AG03991, R01 AG021910, P50 MH071616, U24 RR021382, and R01 MH56584. This databse was supported by NIH R21NS061144 R01NS32979 R01HD057076 U54MH091657 K23DC006638 P50 MH71616 P60 DK020579-31, McDonnell Foundation Collaborative Action Award, NSF IGERT DGE-0548890, Simon’s Foundation Autism Research Initiative grant, Burroughs Wellcome Fund, Charles A. Dana Foundation, Brooks Family Fund, Tourette Syndrome Association, Barnes- Jewish Hospital Foundation, McDonnell Center for Systems Neuroscience, Alvin J. Siteman Cancer Center, American Hearing Research Foundation grant, Diabetes Research and Training Center at Washington University grant. www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen Idee, Bristol-Myers, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, and UCB. Data collected at the Biomedical Imaging Center at the Beckman Institute for Advanced Science and Technology at UIUC. Funded by the Office of Naval Research (ONR): N00014-07-1-0903.
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) Center of Biomedical Research Excellence (COBRE) Cleveland Clinic (Cleveland CCF) Functional Biomedical Informatics Research Network (FBIRN) Information extraction from Images (1X1) KIRBY21
Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD)
Nathan Kline Institute Rockland (NKI-R) sample (phase 1) and NKI-R Enhanced sample (phase 2)
Open access series of imaging studies (OASIS) Power
Parkinson’s Progression Markers Initiative (PPMI)
TRAIN-39
-6.88E-03 -1.33E+02 13223.7
P943
.23, R: .23) for volume. Figure 1 displays age and sex effects on each measure. Conclusions: Formulas derived from these models can be used by other researchers using similar techniques to predict expected surfaces/thicknesses/volumes for new individuals and measure deviations from the normality, according to the study’s characteristics.
P3-278
PROTECTIVE MECHANISMS OF COGNITIVE RESERVE REVEALED BY MULTIMODAL NEUROIMAGING MARKERS
Hwamee Oh1, Qolamreza Razlighi1, Yunglin Gazes1, Christian Habeck1, Yaakov Stern2, 1Columbia University, New York, NY, USA; 2Columbia University College of Physicians and Surgeons, New York, NY, USA. Contact e-mail: [email protected] Background: Several individual differences factors, which are
Int: Intercept. MFS: Magnetic field strength. SD: Standard deviation based on the square root of the mean square error. Sex: men ¼ 1, women ¼ 0. MFS: l.5T ¼ 1, 3T ¼ 0. Manufacturer: GE ¼ 1, Philips ¼ 1, Siemens ¼ 0. Age and TIV are centered by the mean (age - 49.6, TIV -1527850.6).
-4.79E-14 -6.10E+03 -1.50E+04 -1.15E+04 7.44E+03 6.53E+03
5.09E-06 -1.22E-06 -2.87E-02 1.47E-08 -7.15E-14 2.96E-19 -6.14E-02 3.68E-02 -3.64E-02 -6.77E-02 7.24E-02 1.95E+00 -1.73E-01 2.23E+03 1.07E-01 2.47E-08 -5.64E-14 -5.87E+03 -1.72E+04 -1.16E+04 1.05E+04 6.66E+03
2.52E+01 -5.76E+03 -1.86E+03 5.02E+03 -5.76E+02 -5.63E+01 -6.27E+03 -2.14E+03 5.10E+03 -1.24E+02 -6.76E-02 2.16E-02 -4.57E-02 -5.45E-02 8.84E-02 -9.13E-01 -4.20E-02 1.54E+03 3.99E-02 4.33E-09 -9.33E-01 -3.38E-02 1.71E+03 3.98E-02 5.79E-09 7.47E-06 -1.19E-06 -3.01E-02 5.24E-08 -6.74E-14
Left surface 83047.2 -7.98E+01 Right surface 83514.1 -8.54E+01 Left 2.4 -2.52E-03 thickness Right thickness 2.4 -2.42E-03 Left 224167.0 -5.07E+02 volume Right volume 225465.9 -5.09E+02
1.69E+00 -1.65E-01 2.44E+03 1.10E-01 2.88E-08
4671.3 4649.4 0.1 -2.94E+01 -2.87E+01 -4.86E-04
-4.42E-04 0.1 -1.50E+02 13306.9
SD Age Int Cortical measure
Coefficients
Age2
Age3
Sex
TIV
TIV2
TIV3
MFS
GE
Philips
GE X MFS
Philips X MFS
TIV X MFS
Age X Sex
Poster Presentations: Tuesday, July 26, 2016
collectively termed as “reserve”, have been proposed to protect older adults from aging and age-related neurodegenerative diseases. Neural mechanisms underlying the protective pathways mediated by reserve, however, remain to be elucidated. In this study, we examined an association between cognitive reserve (CR) and multimodal neuroimaging markers including beta-amyloid plaques, gray matter (GM) volume, and task-related brain activation to probe neural substrates instantiating reserve. Methods: Forty-six young (mean age ¼ 26, SD ¼ 3) and 65 cognitively normal older adults (mean age ¼ 64.6,SD ¼ 3) underwent two functional magnetic resonance imaging (fMRI) sessions. During fMRI sessions, subjects performed verbal working memory and cognitive control tasks using a letter Sternberg task with varying load (1, 3, or 6 letters) and task-switching (for a single task condition: either vowel/consonant or lower/upper case judgment; for a dual task condition: both tasks), respectively. All older subjects additionally completed 18F-Florbetaben PET and a global amyloid index was computed by an averaged cortical standardized uptake value ratio (SUVR) using a gray matter cerebellum reference region. For each subject, T1-weighted high-resolution structural images were processed using voxel-based morphometry to assess GM volume in a whole-brain template space. A composite score of vocabulary test, American National Adult Reading Test (AMNART), and years of education served as a proxy of CR for each individual. Results: With higher CR, older subjects showed lower amyloid deposition in temporoparietal cortex, parahippocampal gyrus, hippocampus, and left frontal cortex. For wholebrain gray matter volume, higher CR was associated with larger GM volume in lateral occipitotemporal cortex, parahippocampal gyrus, lateral parietal cortex, posterior cingulate, precuneus, and premotor cortex. For task-related activation from both tasks, older adults with higher CR showed less increases in activation as task difficulty increases. Conclusions: The present results suggest that CR may exert its protective role via multiple neural pathways through which the aging brain is protected from accumulation of beta-amyloid and gray matter atrophy, while recruiting brain activation in a more efficient manner. Future studies with longitudinal evaluation will be needed for a more direct assessment of causality.
P3-279
REDUCED SUPRAMARGINAL GYRUS GRAY MATTER VOLUME ASSOCIATED WITH COGNITIVE IMPAIRMENT IN ALZHEIMER’S DISEASE: A 7-TESLA MRI STUDY