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1.  The Alzheimer’s Prevention Initiative composite cognitive test score: Sample size estimates for the evaluation of preclinical Alzheimer’s disease treatments in presenilin 1 E280A mutation carriers 
Objective
There is a need to identify a cognitive composite that is sensitive to tracking preclinical AD decline to be used as a primary endpoint in treatment trials.
Method
We capitalized on longitudinal data, collected from 1995 to 2010, from cognitively unimpaired presenilin 1 (PSEN1) E280A mutation carriers from the world’s largest known early-onset autosomal dominant AD (ADAD) kindred to identify a composite cognitive test with the greatest statistical power to track preclinical AD decline and estimate the number of carriers age 30 and older needed to detect a treatment effect in the Alzheimer’s Prevention Initiative’s (API) preclinical AD treatment trial. The mean-to-standard-deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of one to seven cognitive tests/sub-tests drawn from the neuropsychological test battery in cognitively unimpaired mutation carriers during a two and five year follow-up period, using data from non-carriers during the same time period to correct for aging and practice effects. Combinations that performed well were then evaluated for robustness across follow-up years, occurrence of selected items within top performing combinations and representation of relevant cognitive domains.
Results
This optimal test combination included CERAD Word List Recall, CERAD Boston Naming Test (high frequency items), MMSE Orientation to Time, CERAD Constructional Praxis and Ravens Progressive Matrices (Set A) with an MSDR of 1.62. This composite is more sensitive than using either the CERAD Word List Recall (MSDR=0.38) or the entire CERAD-Col battery (MSDR=0.76). A sample size of 75 cognitively normal PSEN1-E280A mutation carriers age 30 and older per treatment arm allows for a detectable treatment effect of 29% in a 60-month trial (80% power, p=0.05).
Conclusions
We have identified a composite cognitive test score representing multiple cognitive domains that has improved power compared to the most sensitive single test item to track preclinical AD decline in ADAD mutation carriers and evaluate preclinical AD treatments. This API composite cognitive test score will be used as the primary endpoint in the first API trial in cognitively unimpaired ADAD carriers within 15 years of their estimated age at clinical onset. We have independently confirmed our findings in a separate cohort of cognitively healthy older adults who progressed to the clinical stages of late-onset AD, described in a separate report, and continue to refine the composite in independent cohorts and compared with other analytical approaches.
doi:10.4088/JCP.13m08927
PMCID: PMC4331113  PMID: 24816373
composite cognitive score; API; Alzheimer’s Prevention Initiative; E280A; PSEN1; presenilin1; sample size; preclinical; cognitively unimpaired; autosomal dominant; ADAD
2.  Subjective Cognitive Decline: Self and Informant Comparisons 
Background
It is unclear whether self or informant-based subjective cognition better distinguishes emotional factors from early stage Alzheimer’s disease (AD).
Methods
447 healthy members of the Arizona Apolipoprotein E (APOE) Cohort and their informants completed both the self and informant paired Multidimensional Assessment of Neurodegenerative Symptoms questionnaire (MANS).
Results
30.6% of members and 26.2% of informants endorsed decline on the MANS. Both self and informant-based decliners had higher scores of psychological distress and slightly lower cognitive scores than nondecliners. Over the next 6.7 years, 20 developed mild cognitive impairment (MCI). Converters were older at entry than nonconverters (63.8[7.0] vs 58.8[7.3] years, p=.003), 85% were APOE e4 carriers (p<.0001), and they self-endorsed decline earlier than informants (58.9[39.2] vs 28.0[40.4] months before MCI; p=.002).
Conclusions
Both self and informant based subjective decline correlated with greater psychological distress, and slightly lower cognitive performance. Those with incident MCI generally self-endorsed decline earlier than informants.
doi:10.1016/j.jalz.2013.01.003
PMCID: PMC3732500  PMID: 23562429
3.  Fibrillar Amyloid Correlates of Preclinical Cognitive Decline 
Background
It is not known whether preclinical cognitive decline is associated with fibrillar β-amyloid (Aβ) deposition irrespective of Apolipoprotein E (APOE) ε4 status.
Methods
From a prospective observational study of 623 cognitively normal individuals, we identified all subjects who showed preclinical decline of at least 2 standard deviations beyond the decline of the entire group in memory or executive function. Fourteen decliners were matched by APOE ε4 gene dose, age, sex, and education with 14 nondecliners. Dynamic Pittsburgh compound B (PiB) positron emission tomography (PET) scans, the Logan method, statistical parametric mapping, and automatically labeled regions of interest were used to characterize and compare cerebral-to-cerebellar PiB distribution volume ratios (DVR), reflecting fibrillar Aβ burden.
Results
At P<.005 (uncorrected), decliners had significantly greater DVR’s in comparison to nondecliners.
Conclusions
Asymptomatic longitudinal neuropsychological decline is associated with subsequent increased fibrillar amyloid deposition, even when controlling for APOE ε4 genotype.
doi:10.1016/j.jalz.2013.01.009
PMCID: PMC3713087  PMID: 23583233
preclinical; Alzheimer’s disease; amyloid imaging; cognitive decline; Apolipoprotein E
4.  Regional covariance patterns of gray matter alterations in Alzheimer’s disease and its replicability evaluation 
Journal of magnetic resonance imaging : JMRI  2013;39(1):10.1002/jmri.24143.
Purpose
To identify regional network covariance patterns of gray matter associated with Alzheimer’s disease (AD) and to further evaluate its replicability and stability.
Materials and Methods
This study applied a multivariate analytic approach based on scaled subprofile modeling (SSM) to structural MRI data from 19 patients with AD and 19 healthy controls (HC). We further applied the derived covariance patterns to examine the replicability and stability of AD-associated covariance patterns in an independent dataset [13 AD and 14 HC] acquired with a different scanner.
Results
The AD-associated covariance patterns identified from SSM combined principal components mainly involved the temporal lobe and parietal lobe. The expression of covariance patterns was significantly higher in AD patients than HC (t(36) = 5.84, p= 5.75E–7) and predicted the AD/HC group membership (84% sensitivity and 90% specificity). In replicability evaluation, the expression of the forward applied covariance patterns was still statistically significant and had acceptable discriminability (69% sensitivity and 71% specificity).
Conclusion
AD patients showed regional gray matter alterations in a reliable covariance manner. The results suggest that SSM has utility for characterizing covariant features, therefore, can assist us with further understanding covariance patterns of gray matter in AD based on the view of the network.
doi:10.1002/jmri.24143
PMCID: PMC3732807  PMID: 23589138
multivariate analysis; scaled subprofile model; Alzheimer’s disease; structural MRI; voxel-based morphometry
5.  Brain Differences in Infants at Differential Genetic Risk for Late-Onset Alzheimer Disease A Cross-sectional Imaging Study 
JAMA neurology  2014;71(1):11-22.
IMPORTANCE
Converging evidence suggests brain structure alterations may precede overt cognitive impairment in Alzheimer disease by several decades. Early detection of these alterations holds inherent value for the development and evaluation of preventive treatment therapies.
OBJECTIVE
To compare magnetic resonance imaging measurements of white matter myelin water fraction (MWF) and gray matter volume (GMV) in healthy infant carriers and noncarriers of the apolipoprotein E (APOE) ε4 allele, the major susceptibility gene for late-onset AD.
DESIGN, SETTING, AND PARTICIPANTS
Quiet magnetic resonance imaging was performed at an academic research imaging center on 162 healthy, typically developing 2- to 25-month-old infants with no family history of Alzheimer disease or other neurological or psychiatric disorders. Cross-sectional measurements were compared in the APOE ε4 carrier and noncarrier groups. White matter MWF was compared in one hundred sixty-two 2- to 25-month-old sleeping infants (60 ε4 carriers and 102 noncarriers). Gray matter volume was compared in a subset of fifty-nine 6- to 25-month-old infants (23 ε4 carriers and 36 noncarriers), who remained asleep during the scanning session. The carrier and noncarrier groups were matched for age, gestational duration, birth weight, sex ratio, maternal age, education, and socioeconomic status.
MAIN OUTCOMES AND MEASURES
Automated algorithms compared regional white matter MWF and GMV in the carrier and noncarrier groups and characterized their associations with age.
RESULTS
Infant ε4 carriers had lower MWF and GMV measurements than noncarriers in precuneus, posterior/middle cingulate, lateral temporal, and medial occipitotemporal regions, areas preferentially affected by AD, and greater MWF and GMV measurements in extensive frontal regions and measurements were also significant in the subset of 2- to 6-month-old infants (MWF differences, P < .05, after correction for multiple comparisons; GMV differences, P < .001, uncorrected for multiple comparisons). Infant ε4 carriers also exhibited an attenuated relationship between MWF and age in posterior white matter regions.
CONCLUSIONS AND RELEVANCE
While our findings should be considered preliminary, this study demonstrates some of the earliest brain changes associated with the genetic predisposition to AD. It raises new questions about the role of APOE in normal human brain development, the extent to which these processes are related to subsequent AD pathology, and whether they could be targeted by AD prevention therapies.
doi:10.1001/jamaneurol.2013.4544
PMCID: PMC4056558  PMID: 24276092
6.  BRAIN ABNORMALITIES IN YOUNG ADULTS AT GENETIC RISK FOR AUTOSOMAL DOMINANT ALZHEIMER’S DISEASE: A CROSS-SECTIONAL STUDY 
The Lancet. Neurology  2012;11(12):1048-1056.
Summary
Background
We previously detected functional brain imaging abnormalities in young adults at genetic risk for late-onset Alzheimer’s disease (AD). Here, we sought to characterize structural and functional magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), and plasma biomarker abnormalities in young adults at risk for autosomal dominant early-onset AD. Biomarker measurements were characterized and compared in presenilin 1 (PSEN1) E280A mutation carriers and non-carriers from the world’s largest known autosomal dominant early-onset AD kindred, more than two decades before the carriers’ estimated median age of 44 at the onset of mild cognitive impairment (MCI) and before their estimated age of 28 at the onset of amyloid-β (Aβ) plaque deposition.
Methods
Biomarker data for this cross-sectional study were acquired in Antioquia, Colombia between July and August, 2010. Forty-four participants from the Colombian Alzheimer’s Prevention Initiative (API) Registry had structural MRIs, functional MRIs during associative memory encoding/novel viewing and control tasks, and cognitive assessments. They included 20 mutation carriers and 24 non-carriers, who were cognitively normal, 18-26 years old and matched for their gender, age, and educational level. Twenty of the participants, including 10 mutation carriers and 10 non-carriers, had lumbar punctures and venipunctures. Primary outcome measures included task-dependent hippocampal/parahippocampal activations and precuneus/posterior cingulate deactivations, regional gray matter reductions, CSF Aβ1-42, total tau and phospho-tau181 levels, and plasma Aβ1-42 levels and Aβ1-42/Aβ1-40 ratios. Structural and functional MRI data were compared using automated brain mapping algorithms and AD-related search regions. Cognitive and fluid biomarkers were compared using Mann-Whitney tests.
Findings
The mutation carrier and non-carrier groups did not differ significantly in their dementia ratings, neuropsychological test scores, or proportion of apolipoprotein E (APOE) ε4 carriers. Compared to the non-carriers, carriers had higher CSF Aβ1-42 levels (p=0·008), plasma Aβ1-42 levels (p=0·01), and plasma Aβ1-42/Aβ1-40 ratios (p=0·001), consistent with Aβ1-42 overproduction. They also had greater hippocampal/parahippocampal activations (as low as p=0·008, after correction for multiple comparisons), less precuneus/posterior cingulate deactivations (as low as p=0·001, after correction), less gray matter in several regions (p-values <0·005, uncorrected, and corrected p=0·008 in the parietal search region), similar to findings in the later preclinical and clinical stages of autosomal dominant and late-onset AD.
Interpretation
Young adults at genetic risk for autosomal dominant AD have functional and structural MRI abnormalities, along with CSF and plasma biomarker findings consistent with Aβ1-42 over-production. While the extent to which the underlying brain changes are progressive or developmental remain to be determined, this study demonstrates the earliest known biomarker changes in cognitively normal people at genetic risk for autosomal dominant AD.
Funding
Banner Alzheimer’s Foundation, Nomis Foundation, Anonymous Foundation, Forget Me Not Initiative, Boston University Department of Psychology, Colciencias (1115-408-20512, 1115-545-31651), National Institute on Aging (R01 AG031581, P30 AG19610, UO1 AG024904, RO1 AG025526, RF1AG041705), National Institute of Neurological Disorders and Stroke (F31-NS078786) and state of Arizona.
doi:10.1016/S1474-4422(12)70228-4
PMCID: PMC4181671  PMID: 23137948
Alzheimer’s disease; biomarkers; preclinical; early-onset; dominantly inherited; MRI; functional MRI; cerebrospinal fluid; plasma; presenilin E280A mutation; amyloid; tau; genetics; prevention
7.  Fat-Free Body Mass but not Fat Mass is Associated with Reduced Gray Matter Volume of Cortical Brain Regions Implicated in Autonomic and Homeostatic Regulation 
NeuroImage  2012;64:712-721.
Obesity has been associated with alterations of both functional and structural aspects of the human central nervous system. In obese individuals both fat mass (FM; primarily consisting of adipose tissue) and fat-free mass (FFM; all non-adipose tissues) are increased and it remains unknown whether these compartments have separate effects on human brain morphology. We used voxel-based morphometry to investigate the relationships between measures of body composition and regional gray matter volume (GMV) in 76 healthy adults with a wide range of adiposity (24F/52M; age 32.1±8.8y; percentage of body fat [PFAT%] 25.5±10.9%; BMI 29.8±8.9). Faf-free mass index (FFMI kg*m-2) showed negative associations in bilateral temporal regions, the bilateral medial and caudolateral OFC, and the left insula. Fat mass index (FMI kg*m-2) showed similar, but less extensive negative associations within temporal cortical regions and the left caudolateral orbitofrontal cortex (OFC). In addition, negative associations were seen for FMI with GMV of the cerebellum. Associations of FFMI with temporal and medial orbitofrontal GMV appeared to be independent of adiposity. No associations were seen between measures of adiposity (i.e. FM and PFAT) and GMV when adjusted for FFM. The majority of regions that we find associated with FFM have been implicated in the regulation of eating behavior and show extensive projections to central autonomic and homeostatic core structures. These data indicate that not adipose tissue or relative adiposity itself, but obesity related increases in absolute tissue mass and particularly FFM may have a more predominant effect on the human brain. This might be explained by the high metabolic demand of FFM and related increases in total energy needs.
doi:10.1016/j.neuroimage.2012.09.005
PMCID: PMC4178061  PMID: 22974975
Fat-free mass; fat mass; obesity; VBM; MRI; gray matter; prefrontal cortex
8.  A Statistical Parametric Mapping Toolbox Used for Voxel-Wise Analysis of FDG-PET Images of Rat Brain 
PLoS ONE  2014;9(9):e108295.
Purpose
PET (positron emission tomography) imaging researches of functional metabolism using fluorodeoxyglucose (18F-FDG) of animal brain are important in neuroscience studies. FDG-PET imaging studies are often performed on groups of rats, so it is desirable to establish an objective voxel-based statistical methodology for group data analysis.
Material and Methods
This study establishes a statistical parametric mapping (SPM) toolbox (plug-ins) named spmratIHEP for voxel-wise analysis of FDG-PET images of rat brain, in which an FDG-PET template and an intracranial mask image of rat brain in Paxinos & Watson space were constructed, and the default settings were modified according to features of rat brain. Compared to previous studies, our constructed rat brain template comprises not only the cerebrum and cerebellum, but also the whole olfactory bulb which made the later cognitive studies much more exhaustive. And with an intracranial mask image in the template space, the brain tissues of individuals could be extracted automatically. Moreover, an atlas space is used for anatomically labeling the functional findings in the Paxinos & Watson space. In order to standardize the template image with the atlas accurately, a synthetic FDG-PET image with six main anatomy structures is constructed from the atlas, which performs as a target image in the co-registration.
Results
The spatial normalization procedure is evaluated, by which the individual rat brain images could be standardized into the Paxinos & Watson space successfully and the intracranial tissues could also be extracted accurately. The practical usability of this toolbox is evaluated using FDG-PET functional images from rats with left side middle cerebral artery occlusion (MCAO) in comparison to normal control rats. And the two-sample t-test statistical result is almost related to the left side MCA.
Conclusion
We established a toolbox of SPM8 named spmratIHEP for voxel-wise analysis of FDG-PET images of rat brain.
doi:10.1371/journal.pone.0108295
PMCID: PMC4178133  PMID: 25259529
9.  Postprandial Plasma PYY Concentrations are Associated with Increased Regional Gray Matter Volume and rCBF Declines in Caudate Nuclei – a combined MRI and H215O PET study 
NeuroImage  2011;60(1):592-600.
The anorexigenic gastrointestinal hormone Peptide YY plays an important role in the communication between the gastrointestinal tract and the central nervous system. PYY has been shown to modulate brain activity in regions implicated in reward and food related behavior. Its effects on brain structure however, remain unknown. Voxel-based morphometry was used to investigate the relationship between fasting and postprandial plasma PYY concentrations and regional gray matter volume (GMV). For this analysis twenty adult, non diabetic Caucasians were included (18F/2M, age 31±9 y, percentage of body fat [PFAT] 32±8%) who had volumetric brain magnetic resonance images and underwent H215O positron emission tomographic (PET) measurements of regional cerebral blood flow (rCBF), a marker of local neuronal activity, and measurements of plasma total PYY, prior to (fasting) and following a satiating liquid meal. Voxel-wise analysis revealed a regional positive association between postprandial PYY and gray matter volume bilaterally in the caudate nuclei. These associations remained significant (p<0.05) after small volume correction for multiple comparisons. Based on these findings we investigated whether postprandial PYY is associated with PET measured rCBF of the caudate nucleus. We found a significant negative association between average postprandial caudate rCBF and postprandial plasma PYY concentrations (r=−0.60, p<0.02, age, sex and PFAT adjusted). Average postprandial caudate rCBF was also negatively associated with rCBF in the right medial orbitofrontal cortex and the right hippocampal formation (p<0.05, corrected for multiple comparison). Total PYY is positively associated with gray matter but negatively with postprandial activity in the caudate nuclei while caudate activity is negatively associated with rCBF in prefrontal and paralimbic regions implicated in reward behavior. Thus, PYY may act centrally to modulate eating behavior via striatal networks.
doi:10.1016/j.neuroimage.2011.12.023
PMCID: PMC4152947  PMID: 22206963
PYY; caudate nucleus; striatum; gray matter; VBM; MRI; PET; rCBF
10.  Genetic Susceptibility for Alzheimer’s Disease Neuritic Plaque Pathology 
JAMA neurology  2013;70(9):1150-1157.
Objective
To investigate whether Alzheimer’s disease (AD) susceptibility loci from genome-wide association studies (GWAS) impact neuritic plaque pathology and to additionally identify novel risk loci for this trait.
Design
Candidate analysis of single nucleotide polymorphisms (SNPs) and GWAS in a joint clinicopathologic cohort study, followed by targeted validation in independent neuroimaging cohorts.
Participants and Setting
725 deceased subjects from the Religious Orders and Rush Memory and Aging Project, two prospective, community-based studies of aging; the validation neuroimaging cohort consisted of 114 subjects from multiple clinical and research centers.
Main Outcome Measures
A quantitative measure of neuritic plaque pathologic burden, based on assessments of silver-stained tissue averaged from multiple brain regions. Validation based on β-amyloid load by immunocytochemistry, and replication with fibrillar β-amyloid Positron Emission Tomography (PET) imaging with Pittsburgh Compound B or florbetapir.
Results
Besides the previously reported APOE and CR1 loci, we find that ABCA7 (rs3764650, P=0.02) and CD2AP (rs9349407, P=0.03) AD susceptibility loci are associated with neuritic plaque burden. In addition, among the top results of our GWAS, we discovered a novel variant near the amyloid precursor protein gene (APP, rs2829887) that is associated with neuritic plaques (P=3.3×10−6). This polymorphism was associated with postmortem β-amyloid load, as well as fibrillar β-amyloid in two independent cohorts of adults with normal cognition.
Conclusion
These findings enhance understanding of AD risk factors by relating validated susceptibility alleles to increased neuritic plaque pathology and implicate common genetic variation at the APP locus in the earliest, pre-symptomatic stages of AD.
doi:10.1001/jamaneurol.2013.2815
PMCID: PMC3773291  PMID: 23836404
11.  A Rat Brain MRI Template with Digital Stereotaxic Atlas of Fine Anatomical Delineations in Paxinos Space and its Automated Application in Voxel-Wise Analysis 
Human brain mapping  2012;34(6):1306-1318.
This study constructs a rat brain T2-weighted magnetic resonance imaging template including olfactory bulb and a compatible digital atlas. The atlas contains 624 carefully delineated brain structures based on the newest (2005) edition of rat brain atlas by Paxinos and Watson. An automated procedure, as an SPM toolbox, was introduced for spatially normalizing individual rat brains, conducting statistical analysis and visually localizing the results in the Atlas coordinate space. The brain template/atlas and the procedure were evaluated using functional images between rats with the right side middle cerebral artery occlusion (MCAO) and normal controls. The result shows that the brain region with significant signal decline in the MCAO rats was consistent with the occlusion position.
doi:10.1002/hbm.21511
PMCID: PMC4110061  PMID: 22287270
SD rat brain; MRI imaging analysis; SPM; normalization; location; co-registration
12.  Different Patterns of White Matter Disruption among Amnestic Mild Cognitive Impairment Subtypes: Relationship with Neuropsychological Performance 
Amnestic mild cognitive impairment (aMCI) is recognized as the prodromal phase of Alzheimer’s disease (AD). Evidence showed that patients with multiple-domain (MD) aMCI were at higher risk of converting to dementia and exhibited more severe gray matter atrophy than single-domain (SD) aMCI. The investigation of the microstructural abnormalities of white matter (WM) among different subtypes of aMCI and their relations with cognitive performances can help to understand the variations among aMCI subtypes and to construct potential imaging based biomarkers to monitor the progression of aMCI. Diffusion-weighted MRI data were acquired from 40 patients with aMCI (aMCI-SD: n = 19; aMCI-MD: n= 21) and 37 healthy controls (HC). Voxel-wise and atlas-based analyses of whole-brain WM were performed among three groups. The correlations between the altered diffusion metrics of the WM tracts and the neuropsychological scores in each subtype of aMCI were assessed. The aMCI-MD patients showed disrupted integrity in multiple WM tracts across the whole-brain when compared with HCs or with aMCI-SD. In contrast, only few WM regions with diffusion changes were found in aMCI-SD as compared to HCs and with less significance. For neuropsychological correlations, only aMCI-MD patients exhibited significant associations between disrupted WM connectivity (in the body of the corpus callosum and the right anterior internal capsules) and cognitive impairments (MMSE and Digit Symb-Coding scores), whereas no such correlations were found in aMCI-SD. These findings indicate that the degeneration extensively exists in WM tracts in aMCI-MD that precedes the development of AD, whereas underlying WM pathology in aMCI-SD is imperceptible. The results are consistent with the view that aMCI is not a uniform disease entity and presents heterogeneity in the clinical progression.
doi:10.3233/JAD-122023
PMCID: PMC4085483  PMID: 23603396
Amnestic mild cognitive impairment; diffusion tensor imaging; multiple-domain; single-domain; TBSS; white matter
13.  USHERING IN THE STUDY AND TREATMENT OF PRECLINICAL ALZHEIMER DISEASE 
Nature reviews. Neurology  2013;9(7):371-381.
Researchers have begun to characterize the subtle biological and cognitive processes that precede the clinical onset of Alzheimer disease (AD), and to set the stage for accelerated evaluation of experimental treatments to delay the onset, reduce the risk of or completely prevent clinical decline. Here, we provide an overview of the experimental strategies, and brain imaging and cerebrospinal fluid biomarker measures that are used in early detection and tracking of AD, highlighting at-risk individuals who could be suitable for preclinical monitoring. We discuss how these advances have contributed to reconceptualization of AD as a sequence of biological changes that occur during progression from preclinical AD, to mild cognitive impairment and finally dementia, and we review recently proposed research criteria for preclinical AD. Advances in the study of preclinical AD have driven the recognition that efficacy of at least some AD therapies may depend on initiation of treatment before clinical manifestation of disease, leading to a new era of AD prevention research.
doi:10.1038/nrneurol.2013.107
PMCID: PMC4084675  PMID: 23752908
14.  The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer’s Disease 
Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.
doi:10.1109/TCBB.2012.141
PMCID: PMC4085147  PMID: 23702553
Alzheimer’s dementia (AD); multiple indices; multiV-ROC; receiver operational characteristic (ROC)
15.  Brain Differences in Infants at Differential Genetic Risk for Late-Onset Alzheimer Disease 
JAMA neurology  2014;71(1):11-22.
IMPORTANCE
Converging evidence suggests brain structure alterations may precede overt cognitive impairment in Alzheimer disease by several decades. Early detection of these alterations holds inherent value for the development and evaluation of preventive treatment therapies.
OBJECTIVE
To compare magnetic resonance imaging measurements of white matter myelin water fraction (MWF) and gray matter volume (GMV) in healthy infant carriers and noncarriers of the apolipoprotein E (APOE) ε4 allele, the major susceptibility gene for late-onset AD.
DESIGN, SETTING, AND PARTICIPANTS
Quiet magnetic resonance imaging was performed at an academic research imaging center on 162 healthy, typically developing 2- to 25-month-old infants with no family history of Alzheimer disease or other neurological or psychiatric disorders. Cross-sectional measurements were compared in the APOE ε4 carrier and noncarrier groups. White matter MWF was compared in one hundred sixty-two 2- to 25-month-old sleeping infants (60 ε4 carriers and 102 noncarriers). Gray matter volume was compared in a subset of fifty-nine 6- to 25-month-old infants (23 ε4 carriers and 36 noncarriers), who remained asleep during the scanning session. The carrier and noncarrier groups were matched for age, gestational duration, birth weight, sex ratio, maternal age, education, and socioeconomic status.
MAIN OUTCOMES AND MEASURES
Automated algorithms compared regional white matter MWF and GMV in the carrier and noncarrier groups and characterized their associations with age.
RESULTS
Infant ε4 carriers had lower MWF and GMV measurements than noncarriers in precuneus, posterior/middle cingulate, lateral temporal, and medial occipitotemporal regions, areas preferentially affected by AD, and greater MWF and GMV measurements in extensive frontal regions and measurements were also significant in the subset of 2- to 6-month-old infants (MWF differences, P < .05, after correction for multiple comparisons; GMV differences, P < .001, uncorrected for multiple comparisons). Infant ε4 carriers also exhibited an attenuated relationship between MWF and age in posterior white matter regions.
CONCLUSIONS AND RELEVANCE
While our findings should be considered preliminary, this study demonstrates some of the earliest brain changes associated with the genetic predisposition to AD. It raises new questions about the role of APOE in normal human brain development, the extent to which these processes are related to subsequent AD pathology, and whether they could be targeted by AD prevention therapies.
doi:10.1001/jamaneurol.2013.4544
PMCID: PMC4056558  PMID: 24276092
16.  Higher serum glucose levels are associated with cerebral hypometabolism in Alzheimer regions 
Neurology  2013;80(17):1557-1564.
Objective:
To investigate whether higher fasting serum glucose levels in cognitively normal, nondiabetic adults were associated with lower regional cerebral metabolic rate for glucose (rCMRgl) in brain regions preferentially affected by Alzheimer disease (AD).
Methods:
This is a cross-sectional study of 124 cognitively normal persons aged 64 ± 6 years with a first-degree family history of AD, including 61 APOEε4 noncarriers and 63 carriers. An automated brain mapping algorithm characterized and compared correlations between higher fasting serum glucose levels and lower [18F]-fluorodeoxyglucose-PET rCMRgl measurements.
Results:
As predicted, higher fasting serum glucose levels were significantly correlated with lower rCMRgl and were confined to the vicinity of brain regions preferentially affected by AD. A similar pattern of regional correlations occurred in the APOEε4 noncarriers and carriers.
Conclusions:
Higher fasting serum glucose levels in cognitively normal, nondiabetic adults may be associated with AD pathophysiology. Findings suggest that the risk imparted by higher serum glucose levels may be independent of APOEε4 status. This study raises additional questions about the role of the metabolic process in the predisposition to AD and supports the possibility of targeting these processes in presymptomatic AD trials.
doi:10.1212/WNL.0b013e31828f17de
PMCID: PMC3662330  PMID: 23535495
17.  Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition 
Introduction
Based on previous studies, a preclinical classification for Alzheimer’s disease (AD) has been proposed. However, 1) specificity of the different neuronal injury (NI) biomarkers has not been studied, 2) subjects with subtle cognitive impairment but normal NI biomarkers (SCINIB) have not been included in the analyses and 3) progression to mild cognitive impairment (MCI) or dementia of the AD type (DAT), referred to here as MCI/DAT, varies between studies. Therefore, we analyzed data from 486 cognitively normal (CN) and 327 DAT subjects in the AD Neuroimaging Initiative (ADNI)-1/GO/2 cohorts.
Results
In the ADNI-1 cohort (median follow-up of 6 years), 6.3% and 17.0% of the CN subjects developed MCI/DAT after 3 and 5 years follow-up, respectively. NI biomarker cutoffs [structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) tau] were established in DAT patients and memory composite scores were calculated in CN subjects in a cross-sectional sample (n = 160). In the complete longitudinally followed CN ADNI cohort (n = 326, median follow-up of 2 years), CSF and MRI values predicted an increased conversion to MCI/DAT. Different NI biomarkers showed important disagreements for classifying subjects as abnormal NI [kappa = (−0.05)-(0.33)] and into AD preclinical groups. SCINIB subjects (5.0%) were more prevalent than AD preclinical stage 3 subjects (3.4%) and showed a trend for increased progression to MCI/DAT.
Conclusions
Different NI biomarkers lead to different classifications of ADNI subjects, while structural MRI and CSF tau measures showed the strongest predictive value for progression to MCI/DAT. The newly defined SCINIB category of ADNI subjects is more prevalent than AD preclinical stage individuals.
doi:10.1186/2051-5960-2-26
PMCID: PMC4008258  PMID: 24602322
Dementia; Alzheimer’s disease; Magnetic resonance imaging; Cerebrospinal fluid; Amyloid beta; Tau
18.  A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data 
Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research.
doi:10.1109/TPAMI.2012.129
PMCID: PMC3924722  PMID: 22665720
Bayesian network; machine learning; data mining
19.  A Transfer Learning Approach for Network Modeling 
Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature.
doi:10.1080/0740817X.2011.649390
PMCID: PMC3920601  PMID: 24526804
20.  Aging Influence on Gray Matter Structural Associations within the Default Mode Network Utilizing Bayesian Network Modeling 
Recent neuroimaging studies have revealed normal aging-related alterations in functional and structural brain networks such as the default mode network (DMN). However, less is understood about specific brain structural dependencies or interactions between brain regions within the DMN in the normal aging process. In this study, using Bayesian network (BN) modeling, we analyzed gray matter volume data from 109 young and 82 old subjects to characterize the influence of aging on associations between core brain regions within the DMN. Furthermore, we investigated the discriminability of the aging-associated BN models for the young and old groups. Compared to their young counterparts, the old subjects showed significant reductions in connections from right inferior temporal cortex (ITC) to medial prefrontal cortex (mPFC), right hippocampus (HP) to right ITC, and mPFC to posterior cingulate cortex and increases in connections from left HP to mPFC and right inferior parietal cortex to right ITC. Moreover, the classification results showed that the aging-related BN models could predict group membership with 88.48% accuracy, 88.07% sensitivity, and 89.02% specificity. Our findings suggest that structural associations within the DMN may be affected by normal aging and provide crucial information about aging effects on brain structural networks.
doi:10.3389/fnagi.2014.00105
PMCID: PMC4038778  PMID: 24910613
normal aging; Bayesian network modeling; default mode network; structural associations; gray matter
21.  Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks 
Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20–28) and old (N = 82; mean age =74.37 years, range 60–90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.
doi:10.3389/fncom.2014.00122
PMCID: PMC4179716  PMID: 25324771
aging; Bayesian networks; structural association; gray matter volume; structural networks
22.  A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data 
The Independent Component Analysis (ICA)—linear non-Gaussian acyclic model (LiNGAM), an algorithm that can be used to estimate the causal relationship among non-Gaussian distributed data, has the potential value to detect the effective connectivity of human brain areas. Under the assumptions that (a): the data generating process is linear, (b) there are no unobserved confounders, and (c) data have non-Gaussian distributions, LiNGAM can be used to discover the complete causal structure of data. Previous studies reveal that the algorithm could perform well when the data points being analyzed is relatively long. However, there are too few data points in most neuroimaging recordings, especially functional magnetic resonance imaging (fMRI), to allow the algorithm to converge. Smith's study speculates a method by pooling data points across subjects may be useful to address this issue (Smith et al., 2011). Thus, this study focus on validating Smith's proposal of pooling data points across subjects for the use of LiNGAM, and this method is named as pooling-LiNGAM (pLiNGAM). Using both simulated and real fMRI data, our current study demonstrates the feasibility and efficiency of the pLiNGAM on the effective connectivity estimation.
doi:10.3389/fncom.2014.00125
PMCID: PMC4186480  PMID: 25339895
effective connectivity; causal structure; group analysis; functional magnetic resonance imaging (fMRI); linear non-Gaussian acyclic model (LiNGAM); pooling-LiNGAM (pLiNGAM)
23.  Combining Multiple Markers to Improve the Longitudinal Rate of Progression-Application to Clinical Trials on the Early Stage of Alzheimer’s Disease 
Statistics in biopharmaceutical research  2013;5(1):10.1080/19466315.2012.756662.
Clinical trials on early stage Alzheimer’s disease (AD) are reaching a bottleneck because none of the current disease markers changes appreciably early in the disease process and therefore a huge sample is required to adequately power such trials. We propose a method to combine multiple markers so that the longitudinal rate of progression can be improved. The criterion is to maximize the probability that the combined marker will be decreased over time (assuming a negative mean slope for each marker). We propose estimates to the weights of markers in the optimum combination and a confidence interval estimate to the combined rate of progression through the maximum likelihood estimates and a bootstrap procedure. We conduct simulations to assess the performance of our estimates and compare our approach with the first principal component from a principal component analysis. The proposed method is applied to a real world sample of individuals with preclinical AD to combine measures from two cognitive domains. The combined cognitive marker is finally used to design future clinical trials on preclinical AD, demonstrating a significant improvement in reducing the sample sizes needed to power such trials when compared with individual markers alone.
doi:10.1080/19466315.2012.756662
PMCID: PMC3868484  PMID: 24363827
Bootstrap estimate; Delta method; Multivariate random coefficients models; Power; Preclinical Alzheimer’s disease (AD); Randomized clinical trials (RCT); Sample size
24.  Florbetapir PET analysis of amyloid-β deposition in the presenilin 1 E280A autosomal dominant Alzheimer’s disease kindred: a cross-sectional study 
Lancet neurology  2012;11(12):10.1016/S1474-4422(12)70227-2.
Summary
Background
Fibrillar amyloid-β (Aβ) is thought to begin accumulating in the brain many years before the onset of clinical impairment in patients with Alzheimer’s disease. By assessing the accumulation of Aβ in people at risk of genetic forms of Alzheimer’s disease, we can identify how early preclinical changes start in individuals certain to develop dementia later in life. We sought to characterise the age-related accumulation of Aβ deposition in presenilin 1 (PSEN1) E280A mutation carriers across the spectrum of preclinical disease.
Methods
Between Aug 1 and Dec 6, 2011, members of the familial Alzheimer’s disease Colombian kindred aged 18–60 years were recruited from the Alzheimer’s Prevention Initiative’s registry at the University of Antioquia, Medellín, Colombia. Cross-sectional assessment using florbetapir PET was done in symptomatic mutation carriers with mild cognitive impairment or mild dementia, asymptomatic carriers, and asymptomatic non-carriers. These assessments were done at the Banner Alzheimer’s Institute in Phoenix, AZ, USA. A cortical grey matter mask consisting of six predefined regions. was used to measure mean cortical florbetapir PET binding. Cortical-to-pontine standard-uptake value ratios were used to characterise the cross-sectional accumulation of fibrillar Aβ deposition in carriers and non-carriers with regression analysis and to estimate the trajectories of fibrillar Aβ deposition.
Findings
We enrolled a cohort of 11 symptomatic individuals, 19 presymptomatic mutation carriers, and 20 asymptomatic non-carriers, ranging in age from 20 to 56 years. There was greater florbetapir binding in asymptomatic PSEN1 E280A mutation carriers than in age matched non-carriers. Fibrillar Aβ began to accumulate in PSEN 1E280A mutation carriers at a mean age of 28·2 years (95% CI 27·3–33·4), about 16 years and 21 years before the predicted median ages at mild cognitive impairment and dementia onset, respectively. 18F florbetapir binding rose steeply over the next 9·4 years and plateaued at a mean age of 37·6 years (95% CI 35·3–40·2), about 6 and 11 years before the expected respective median ages at mild cognitive impairment and dementia onset. Prominent florbetapir binding was seen in the anterior and posterior cingulate, precuneus, and parietotemporal and frontal grey matter, as well as in the basal ganglia. Binding in the basal ganglia was not seen earlier or more prominently than in other regions.
Interpretation
These findings contribute to the understanding of preclinical familial Alzheimer’s disease and help set the stage for assessment of amyloid-modifying treatments in the prevention of familial Alzheimer’s disease.
Funding
Avid Radiopharmaceuticals, Banner Alzheimer’s Foundation, Nomis Foundation, Anonymous Foundation, Forget Me Not Initiative, Colciencias, National Institute on Aging, and the State of Arizona.
doi:10.1016/S1474-4422(12)70227-2
PMCID: PMC3515078  PMID: 23137949
25.  Gray matter network associated with risk for Alzheimer's disease in young to middle-aged adults 
Neurobiology of aging  2012;33(12):2723-2732.
The apolipoprotein E (APOE) ε4 allele increases the risk for late-onset Alzheimer's disease (AD) and age-related cognitive decline. We investigated whether ε4 carriers show reductions in gray matter volume compared to ε4 non-carriers decades prior to the potential onset of AD dementia or healthy cognitive aging. Fourteen cognitively normal ε4 carriers, ages 26 to 45, were compared with 10 age-matched, ε4 non-carriers using T1-weighted volumetric magnetic resonance imaging (MRI) scans. All had reported first or second-degree family histories of dementia. Group differences in gray matter were tested using voxel-based morphometry (VBM) and a multivariate model of regional covariance, the Scaled Subprofile Model (SSM). A combination of the first two SSM MRI gray matter patterns distinguished the APOE ε4 carriers from non-carriers. This combined pattern showed gray matter reductions in bilateral dorsolateral and medial frontal, anterior cingulate, parietal, and lateral temporal cortices with co-varying relative increases in cerebellum, occipital, fusiform, and hippocampal regions. With these gray matter differences occurring decades prior to the potential onset of dementia or cognitive aging, the results suggest longstanding, gene-associated differences in brain morphology that may lead to preferential vulnerability for the later effects of late onset AD or healthy brain aging.
doi:10.1016/j.neurobiolaging.2012.01.014
PMCID: PMC3398228  PMID: 22405043
Apolipoprotein E; Late-Onset Alzheimer's Disease; Magnetic Resonance Imaging; Voxel-Based Morphometry; Multivariate Analysis; Gray Matter Volume

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