Coevolution of brain structures in mild cognitive impairment

P26

Alzheimer’s Imaging Consortium Posters: IC-P

brain amyloidosis for enrollment. The identification of inexpensive and non-invasive screening variables that could predict which individuals have significant amyloid accumulation would reduce screening costs. Methods: 483 cognitively normal (CN) individuals, aged 70-92, from the population-based Mayo Clinic Study of Aging underwent PIB-PET imaging. Logistic regression was used to determine whether age, sex, APOE genotype, family history, or cognitive performance were associated with increased odds of a PIB retention ratio>1.4 and >1.5. Area under the receiver operating characteristic curve (AUROC) evaluated the discrimination between PIB positive and negative subjects. Positive (PPV) and negative (NPV) predictive value was defined based on an estimated probability >0.50 who were PIB-positive. The estimated sample size for each characteristic, by age group (70-79 and 80-89 years), needed to screen to enroll 100 participants into a clinical trial with PIB>1.4 or >1.5 was determined based on the desired sample size divided by sample proportions in the MCSA data. Results: Of 483 CN individuals, 151 (31%) had PIB>1.5 and 211 (44%)>1.4. In univariate and multivariate models, discrimination was modest (AUROCw0.6-0.7). Multivariately, age and APOE best predicted odds of PIB>1.4 and >1.5. For PIB>1.5, the addition of all factors resulted in a PPV of 60% and NPV of 74%, and reduced the number needed to screen from 320 to 166 to enroll 100 individuals into a pre-clinical AD trial requiring brain amyloidosis. The predictability of some factors varied with age. For example, based on PIB>1.5, information on APOE genotype alone reduced the number needed to screen by 48% in persons aged 70-79 and 33% in those aged 80-89. Conclusions: Age and APOE genotype are useful predictors of amyloid accumulation, but discrimination is modest. Nonetheless, these results suggest that inexpensive and non-invasive measures could significantly reduce the number of CN individuals needed to screen with amyloid PET imaging or a lumbar puncture for CSF to identify a given number of amyloid positive subjects.

IC-P-039

REGIONAL CORTICAL THINNING PREDICTS WORSENING APATHY AND HALLUCINATIONS IN MILD COGNITIVE IMPAIRMENT AND MILD ALZHEIMER’S DISEASE

Nancy Donovan1, Lauren Wadsworth2, Natacha Lorius3, Joseph Locascio4, Dorene Rentz5, Keith Johnson6, Reisa Sperling7, Gad Marshall7, 1Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School and Cambridge Health Alliance, Boston, Massachusetts, United States; 2Massachusetts General Hospital, Charlestown, Massachusetts, United States; 3Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, Massachusetts, United States; 4Massachusetts General Hospital, Boston, Massachusetts, United States; 5Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States; 6Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States; 7Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States. Background: Apathy and hallucinations are debilitating neuropsychiatric symptoms accompanying cognitive and functional decline in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. Prior cross-sectional studies of apathy in AD dementia have most consistently implicated structural and functional changes in anterior cingulate and medial orbitofrontal cortices. The pathophysiological basis for hallucinations in AD is poorly understood. The objective of this study was to examine magnetic resonance imaging (MRI) cortical thickness and cerebrospinal fluid (CSF) AD biomarkers in relation to apathy and hallucinations, cross-sectionally and longitudinally, in a continuum of individuals with normal cognition (NC), MCI, and mild AD dementia. Methods: Eight hundred and twelve subjects from the Alzheimer’s Disease Neuroimaging Initiative study (229 NC, 395 MCI, 188 AD) underwent structural MRI at baseline and clinical assessments at baseline and longitudinally up to 3 years. CSF abeta, total tau, and phospho-tau were obtained for a subset of 413 subjects at baseline.

Backward elimination mixed random/fixed coefficient longitudinal regression models were used to evaluate the relationships between baseline cortical thickness in 6 regions (anterior cingulate, medial orbitofrontal, dorsolateral prefrontal, supramarginal, inferior temporal, occipital) and CSF biomarkers versus change in apathy and hallucinations measured by the Neuropsychiatric Inventory-Questionnaire. Covariates included the baseline dependent variable, diagnosis, gender, age, Apolipoprotein E, premorbid intelligence, memory performance, executive function, antidepressant use, and AD duration. General linear regression models were used to examine analogous cross-sectional associations at baseline. Results: Reduced baseline inferior temporal cortical thickness was predictive of increasing apathy over time (P <0.0001; R 2 ¼ 0.59 for full model with random and fixed terms), while reduced supramarginal cortical thickness was predictive of increasing hallucinations over time (P ¼ 0.04 ; R 2 ¼ 0.66 for model). There was no association with cortical thickness cross-sectionally. CSF biomarkers were not related to apathy or hallucinations severity in cross-sectional or longitudinal analyses. Conclusions: These results suggest that temporal and parietal cortical thinning is associated with worsening apathy and hallucinations in a large cohort across the AD spectrum. CSF AD biomarkers did not show associations with these neuropsychiatric symptoms. Additional longitudinal studies may further elucidate the expression and time course of these debilitating symptoms in relation to AD biomarkers.

IC-P-040

COEVOLUTION OF BRAIN STRUCTURES IN MILD COGNITIVE IMPAIRMENT

Owen Carmichael1, Donald McLaren2, Douglas Tommet3, Dan Mungas4, Richard Jones3, 1University of California, Davis, Davis, California, United States; 2Massachusetts General Hospital and Harvard Medical School, Bedford, Massachusetts, United States; 3Institute for Aging Research, Boston, Massachusetts, United States; 4University of California, Davis, Sacramento, California, United States. Background: Network accounts of Alzheimer’s disease (AD), based on cross-sectional brain imaging observations, postulate that the biological course of the disease is characterized by coordinated spatial patterns of brain change to distributed cognitive networks. We tested this conjecture by applying data driven techniques to identify spatial patterns of correlated longitudinal brain change over a 2-year period, based on structural magnetic

Table 1 Factor loadings in the four-factor model for longitudinal brain change. Factor Slope

1

2

3

4

Bank of Sup. Temporal Sul. Inferior Parietal Middle Temporal Precuneus Fusiform Inferior Temporal Superior Marginal Isthmus of Cingulate Posterior Cingulate Hippocampus Pars Opercularis Rostral Anterior Cingulate Pars Triangularis Superior Frontal Middle Frontal Pre-Central -Orbital Frontal Frontal Pole

0.75 0.71 0.70 0.68 0.66 0.63 0.61 0.48 0.48 0.39 0.15 -0.13 -0.00 0.19 0.11 0.02 0.05 -0.17

0.15 -0.04 0.09 0.14 -0.02 0.06 0.34 0.09 0.28 -0.06 0.80 0.76 0.76 0.72 0.69 0.62 0.61 0.58

0.02 0.02 0.49 0.08 -0.01 0.31 0.24 -0.06 -0.05 0.42 -0.00 0.41 0.24 -0.13 -0.04 0.28 -0.05 0.06 -0.04 0.34 -0.02 -0.13 -0.04 0.26 0.08 0.07 0.14 -0.04 0.19 0.06 0.41 -0.10 -0.04 0.33 0.20 0.30 (Continued )

Alzheimer’s Imaging Consortium Posters: IC-P Table 1

Factor loadings in the four-factor model for longitudinal brain change. (Continued ) Factor Slope

1

2

3

4

Insula Caudal Middle Frontal Medial Orbital Frontal Caudal Anterior Cingulate Transverse Temporal Superior Temporal Sulcus Cuneus Superior Parietal Post-Central Lateral Occipital Paracentral Lingual Parahippocampal Temporal Pole Entorhinal

0.37 0.24 0.02 0.04 0.08 0.34 0.01 0.38 -0.05 0.18 -0.02 -0.05 0.35 0.15 0.34

0.56 0.56 0.55 0.55 0.49 0.42 -0.16 0.06 0.42 0.02 0.39 0.05 -0.03 0.21 -0.07

-0.32 0.31 -0.06 -0.13 0.17 0.01 0.65 0.63 0.56 0.53 0.41 0.44 0.01 -0.09 0.05

0.05 -0.10 0.36 0.15 0.12 0.38 0.40 0.01 -0.03 0.52 0.01 0.65 0.58 0.52 0.48

Factor loadings greater than .4 are shown in bold. Regions that have high loadings in the same factor have a greater degree of correlated brain change over two years.

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resonance imaging (MRI). Methods: We quantified inter-regional covariance in cortical gray matter changes in 313 Alzheimer’s Disease Neuroimaging Initiative participants who were clinically diagnosed with amnestic mild cognitive impairment at baseline and underwent serial MRI at 6-month intervals over the course of 2 years. A set of 35 bilateral cortical gray matter region volumes were estimated for each MRI using FreeSurfer. Baseline region volumes and rates of change in volume over 2 years were derived from region specific growth curve models. The covariance of the rates of change between regions was analyzed with exploratory structural equation modeling (ESEM). The ESEM model was used to estimate a factor analysis model with pre-specified residual covariance structure to identify factors (i.e. groupings of regions) that exhibited highly correlated rates of change. Results: A four-factor model provided the best account of regional changes: this model exhibited adequate fit (CFI ¼ 0.965, RMSEA ¼ 0.06) and minimized the Bayesian Information Criterion over all models between 1 and 5 factors (see Table and Figure). The four factors approximately corresponded to co-occurring change within the prefrontal cortex; medial temporal lobe; posterior default mode network (i.e., posterior cingulate, precuneus, and inferior parietal regions); and regions largely spared by the early pathological course of AD (i.e., sensorimotor and occipital cortex). Conclusions: The data-driven observation of coordinated “frontal aging” superimposed upon traditional early-AD atrophy and default mode network changes supports the view that in individuals at high risk of eventual clinical AD, multiple co-occurring patterns of distributed neuronal death may be detectable. These coordinated changes may correspond to differing biological substrates and differing cognitive consequences. Brain structural changes in AD may be best modeled in terms of co-occurring damage to multiple distributed cognitive networks.

IC-P-041

PREDICTION OF DISEASE PROGRESSION IN MILD COGNITIVE IMPAIRMENT FROM VMRI AND CONCORDANCE WITH CSF BIOMARKERS

Peng Yu1, Adam Schwarz1, Jia Sun1, Laurel Beckett2, Thomas Kelleher3, Huanli Wang4, Chris Davidson5, Denise Frank5, Clifford Jack6, Patricia Cole7, Derek Hill8, 1Eli Lilly and Company, Indianapolis, Indiana, United States; 2University of California Davis, Oakland, California, United States; 3Bristol-Myers Squibb, Wallingford, Connecticut, United States; 4 University of California, Davis, Davis, California, United States; 5Critical Path Institute, Tucson, Arizona, United States; 6Mayo Clinic, Rochester, Minnesota, United States; 7Imagepace, Cincinnati, Ohio, United States; 8 University College of London and IXICO Ltd., London, United Kingdom.

Figure 1. Visual depiction of the four groupings (factors) of regions that showed strong correlation in rates of change according to ESEM modeling. For each such factor, regions with factor loadings greater than .4 are shown with a yellow box.

Background: There is growing interest in the use of structural MRI (and hippocampal volume (HCV) in particular) as an enrichment biomarker for clinical trials. However, there remains no standard definition of the hippocampus and a number of different algorithms are in use for the quantification of HCV. It is unclear how the enrichment performance depends on the algorithm employed.Secondly, the concordance in subjects selected using biomarkers reflecting neurodegeneration (HCV, CSF (p)tau) and abnormal amyloid trafficking (CSF Ab) has implications for enrichment given their different dynamics with respect to disease stage. Methods: We performed Cox regression analyses of HCV and CSF analytes, and their combination, as predictors of clinical progression in the ADNI-1 MCI population. MRI acquisition and image post-processing were identical; HCV was computed from ADNI 1.5T MRI data using four (semi-)automated algorithms: Freesurfer, NeuroQuant, HMAPS and LEAP. Covariates included age, gender, ADAS-Cog, ApoE status and intracranial volume. Training and testing sets were predefined independently by the ADNI statistics core team and resulted in n ¼ 120-150/160-170 (training/testing). Similar analyses were performed on CSF biomarkers with n ¼ 68-71/92-93 (training/testing). Prediction performance was calculated with respect to two outcome measures: (1) conversion to clinical dementia within 2 years and (2) progression by 2 points on the CDR-SB scale. Concordance was measured using percentage overlap in predicted converters and kappa coefficients (statistical measure of inter-rater agreement). Results: Prediction performance with respect to both conversion to dementia and progression on CDR-SB was