Functional magnetic resonance imaging (fMRI) was used to study memory-associated activation of medial temporal lobe (MTL) regions in 32 nondemented elderly individuals with mild cognitive impairment (MCI). Subjects performed a visual encoding task during fMRI scanning and were tested for recognition of stimuli afterward. MTL regions of interest were identified from each individual’s structural MRI, and activation was quantified within each region. Greater extent of activation within the hippocampal formation and parahippocampal gyrus (PHG) was correlated with better memory performance. There was, however, a paradoxical relationship between extent of activation and clinical status at both baseline and follow-up evaluations. Subjects with greater clinical impairment, based on the Clinical Dementia Rating Sum of Boxes, recruited a larger extent of the right PHG during encoding, even after accounting for atrophy. Moreover, those who subsequently declined over the 2.5 years of clinical follow-up (44% of the subjects) activated a significantly greater extent of the right PHG during encoding, despite equivalent memory performance. We hypothesize that increased activation in MTL regions reflects a compensatory response to accumulating AD pathology and may serve as a marker for impending clinical decline.
Impairment in executive function (EF) is commonly found in Alzheimer’s Dementia (AD) and Mild Cognitive Impairment (MCI). Atlas-based Diffusion Tensor Imaging (DTI) methods may be useful in relating regional integrity to EF measures in MCI and AD.
66 participants (25 NC, 22 MCI, and 19 AD) received DTI scans and clinical evaluation. DTI scans were applied to a pre-segmented atlas and fractional anisotropy (FA) and mean diffusivity (MD) were calculated. ANOVA was used to assess group differences in frontal, parietal, and cerebellar regions. For regions differing between groups (p<0.01), linear regression examined the relationship between EF scores and regional FA and MD.
Anisotropy and diffusivity in frontal and parietal lobe white matter (WM) structures were associated with EF scores in MCI and only frontal lobe structures in AD. EF was more strongly associated with FA than MD. The relationship between EF and anisotropy and diffusivity was strongest in MCI. These results suggest that regional WM integrity is compromised in MCI and AD and that FA may be a better correlate of EF than MD.
Executive Function; DTI; Alzheimer’s disease; MCI
Among the major impediments to the design of clinical trials for the prevention of Alzheimer's disease (AD), the most critical is the lack of validated biomarkers, assessment tools, and algorithms that would facilitate identification of asymptomatic individuals with elevated risk who might be recruited as study volunteers. Thus, the Leon Thal Symposium 2009 (LTS'09), on October 27–28, 2009 in Las Vegas, Nevada, was convened to explore strategies to surmount the barriers in designing a multisite, comparative study to evaluate and validate various approaches for detecting and selecting asymptomatic people at risk for cognitive disorders/dementia. The deliberations of LTS'09 included presentations and reviews of different approaches (algorithms, biomarkers, or measures) for identifying asymptomatic individuals at elevated risk for AD who would be candidates for longitudinal or prevention studies. The key nested recommendations of LTS'09 included: (1) establishment of a National Database for Longitudinal Studies as a shared research core resource; (2) launch of a large collaborative study that will compare multiple screening approaches and biomarkers to determine the best method for identifying asymptomatic people at risk for AD; (3) initiation of a Global Database that extends the concept of the National Database for Longitudinal Studies for longitudinal studies beyond the United States; and (4) development of an educational campaign that will address public misconceptions about AD and promote healthy brain aging.
Alzheimer's disease; Dementia; Mild cognitive impairment; Prevention; Biomarkers; Diagnosis; Screening; Clinical trials; MCI; Asymptomatic; Risk factors; Registry; Longitudinal studies; Database; PAD2020; Leon Thal Symposium; Treatment; Drug development; Health policy
MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. But, the image contrast is influenced by both inherent MR contrasts and residual anatomical variability after the registration; in addition, the topology of the brain structures cannot reliably be preserved. In this study, we demonstrated a population-based template-creation approach, which is based on Bayesian template estimation on a diffeomorphic random orbit model. This approach attempts to define a population-representative template without the cross-subject intensity averaging; thus, the topology of the brain structures is preserved. It has been tested for segmented brain structures, such as the hippocampus, but its validity on whole-brain MR images has not been examined. This paper validates and evaluates this atlas generation approach, i.e., Volume-based Template Estimation (VTE). Using datasets from normal subjects and Alzheimer's patients, quantitative measurements of sub-cortical structural volumes, metric distance, displacement vector, and Jacobian were examined to validate the group-averaged shape features of the VTE. In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1 mm for these population-preserved cortical structures in an elderly population.
Volume-based Template Estimation (VTE); study-specific atlas; MRI; volume-surface analysis
Levels of β-amyloid and phosphorylated tau (p-tau), as measured in cerebrospinal fluid (CSF), have been associated with risk of progressing from normal cognition to onset of clinical symptoms during preclinical Alzheimer’s disease (AD). We examined whether cognitive reserve (CR) modifies this association. CSF was obtained at baseline from 239 participants (mean age 57.2 years) who had been followed for up to 17 years with clinical and cognitive assessments (mean follow-up 8 years). A composite score based on the National Adult Reading Test (NART), vocabulary, and years of education at baseline was used as an index of CR. Cox regression models showed that increased risk of progressing from normal cognition to symptom onset was associated with lower CR, lower baseline β-amyloid, and higher baseline p-tau. There was no interaction between CR and β-amyloid, suggesting that the protective effects of higher CR are equivalent across the observed range of amyloid levels. By contrast, both tau and p-tau interacted with CR, indicating that CR was more protective at lower levels of tau and p-tau.
cognitive reserve; preclinical Alzheimer’s disease; Mild Cognitive Impairment; cerebrospinal fluid; tau; amyloid; cohort studies; biomarkers
This study evaluated longitudinal CSF biomarker measures collected when participants were cognitively normal to determine the magnitude and time course of biomarker changes before the onset of clinical symptoms in subjects with mild cognitive impairment (MCI).
Longitudinal CSF collection and cognitive assessments were performed on a cohort of 265 participants who were cognitively normal at their baseline assessment and subsequently developed MCI or dementia. CSF β-amyloid 1–42 (Aβ1–42), total tau (t-tau), and phosphorylated tau (p-tau) were determined longitudinally. Consensus diagnoses were completed annually. Cox regression analyses were performed, with baseline CSF values and time-dependent rate of change in CSF values as covariates (adjusted by baseline age, race, and education), in relation to time to onset of mild cognitive symptoms.
The mean time from baseline to onset of mild cognitive symptoms was 5.41 years. Increased risk of progressing from normal cognition to onset of clinical symptoms was associated with baseline values of Aβ1–42, p-tau, and the ratios of p-tau/Aβ1–42 and t-tau/Aβ1–42 (p < 0.002). Additionally, the rate of change in the ratios of t-tau/Aβ1–42 (p < 0.004) and p-tau/Aβ1–42 (p < 0.02) was greater among participants who were subsequently diagnosed with MCI.
Baseline differences in CSF values were predictive of clinical symptoms that were a harbinger of a diagnosis of MCI more than 5 years before symptom onset, and continue to show longitudinal changes as cognitive symptoms develop, demonstrating that baseline and longitudinal changes in CSF biomarkers are evident during the preclinical phase of Alzheimer disease.
Randomized-controlled trials that examine the effects of Cholinesterase inhibitors (ChEI) and memantine on patient outcomes over long periods of time are difficult to conduct. Observational studies based on practice-based populations outside the context of controlled trials and open label extension studies that evaluate the effects of these medications over time are limited.
To examine in an observational study (1) relationships between ChEI and memantine use and functional and cognitive endpoints and mortality in AD patients, (2) relationships between other patient characteristics on these clinical endpoints, and (3) whether effects of the predictors change across time.
Multicenter, natural history study.
Three university-based AD centers in the US.
201 patients diagnosed with probable AD with modified Mini-Mental State Examination scores of 30 or higher at study entry followed annually for 6 years.
Discrete-time hazard analyses were used to examine relationships between ChEI and memantine use during the previous 6 months reported at each assessment and time to cognitive (Mini-Mental State Examination, MMSE≤10) and functional (Blessed Dementia Rating Scale, BDRS≥10) endpoints and mortality. Analyses controlled for clinical characteristics including baseline cognition, function, and comorbid conditions, and presence of extrapyramidal signs and psychiatric symptoms at each assessment interval. Demographic characteristics included baseline age, sex, education, and living arrangement at each assessment interval.
ChEI use was associated with delayed time in reaching functional endpoint and death. Memantine use was associated with delayed time to death. Different patient characteristics were associated with different clinical endpoints
Results suggest long term beneficial effects of ChEI and memantine on patient outcomes. As for all observational cohort study, observed relationships should not be interpreted as causal effects.
Alzheimer’s disease; cholinesterase inhibitors; memantine; outcomes; longitudinal studies
A high body mass index (BMI) in middle-age or a decrease in BMI at late-age has been considered a predictor for the development of Alzheimer's disease (AD). However, little is known about the BMI change close to or after AD onset.
BMI of participants from three cohorts, the Washington Heights and Inwood Columbia Aging Project (WHICAP; population-based) and the Predictors Study (clinic-based), and National Alzheimer's Coordinating Center (NACC; clinic-based) were analyzed longitudinally. We used generalized estimating equations to test whether there were significant changes of BMI over time, adjusting for age, sex, education, race, and research center. Stratification analyses were run to determine whether BMI changes depended on baseline BMI status.
BMI declined over time up to AD clinical onset, with an annual decrease of 0.21 (p=0.02) in WHICAP and 0.18 (p=0.04) kg/m2 in NACC. After clinical onset of AD, there was no significant decrease of BMI. BMI even increased (b=0.11, p=0.004) among prevalent AD participants in NACC. During the prodromal period, BMI decreased over time in overweight(BMI ≥25 and <30) WHICAP participants or obese (BMI≥30) NACC participants. After AD onset, BMI tended to increase in underweight/normal weight (BMI<25) patients and decrease in obese patients in all three cohorts, although the results were significant in NACC study only.
Our study suggests that while BMI declines before the clinical AD onset, it levels off after clinical AD onset, and might even increase in prevalent AD. The pattern of BMI change may also depend on the initial BMI.
Body mass index; weight; Alzheimer's disease; prospective study
The prevalence of both type II diabetes mellitus (DM) and cognitive impairment is high and increasing in older adults. We examined the extent to which DM diagnosis was associated with poorer cognitive performance and dementia diagnosis in a population-based cohort of US older adults.
We studied 7,606 participants in the National Health and Aging Trends Study, a nationally representative cohort of Medicare beneficiaries aged 65 years and older. DM and dementia diagnosis were based on self-report from participants or proxy respondents, and participants completed a word-list memory test, the Clock Drawing Test, and gave a subjective assessment of their own memory.
In unadjusted analyses, self-reported DM diagnosis was associated with poorer immediate and delayed word recall, worse performance on the Clock Drawing Test, and poorer self-rated memory. After adjusting for demographic characteristics, body mass index, depression and anxiety symptoms, and medical conditions, DM was associated with poorer immediate and delayed word recall and poorer self-rated memory, but not with the Clock Drawing Test performance or self-reported dementia diagnosis. After excluding participants with a history of stroke, DM diagnosis was associated with poorer immediate and delayed word recall and the Clock Drawing Test performance, and poorer self-rated memory, but not with self-reported dementia diagnosis.
In this recent representative sample of older Medicare enrollees, self-reported DM was associated with poorer cognitive test performance. Findings provide further support for DM as a potential risk factor for poor cognitive outcomes. Studies are needed that investigate whether DM treatment prevents cognitive decline.
dementia; Alzheimer's disease; cognition; type II diabetes
The APOE ε4 allele increases the risk of developing Alzheimer’s disease, whereas the APOE ε2 allele reduces risk. We examined whether cognitive reserve (CR), as measured by an index consisting of education, reading, and vocabulary, modifies these associations. CR was measured at baseline in 257 cognitively normal individuals (mean age 57.2 years) who have been followed for up to 17 years (mean follow-up = 9.2 years). Cox regression models showed that CR and APOE ε4 independently affected the risk of progressing from normal cognition to onset of clinical symptoms: CR reduced risk by about 50% in both ε4 carriers and non-carriers, while ε4 increased risk by about 150%. In contrast, APOE ε2 interacted with CR, such that CR was more protective in ε2 carriers than non-carriers. This suggests that individuals with an ε2 genotype may disproportionately benefit from lifetime experiences that enhance cognition.
Surface-based deformation markers obtained from diffeomorphic mapping of the amygdala are used to study specific atrophy patterns in a combined mild cognitively impaired and demented cohort compared with cognitively normal aging subjects. Statistical analysis demonstrates with high significance in a small sample of legacy data that deformation-based morphometry provides sensitive markers for locating atrophy in the amygdala. With respect to a high-field amygdala atlas, significant atrophy was found in the basomedial and lateral nucleus subregions.
The demand for rapidly administered, sensitive, and reliable cognitive assessments that are specifically designed for identifying individuals in the earliest stages of cognitive decline (and to measure subtle change over time) has escalated as the emphasis in Alzheimer’s disease clinical research has shifted from clinical diagnosis and treatment toward the goal of developing presymptomatic neuroprotective therapies. To meet these changing clinical requirements, cognitive measures or tailored batteries of tests must be validated and determined to be fit-for-use for the discrimination between cognitively healthy individuals and persons who are experiencing very subtle cognitive changes that likely signal the emergence of early mild cognitive impairment. We sought to collect and review data systematically from a wide variety of (mostly computer-administered) cognitive measures, all of which are currently marketed or distributed with the claims that these instruments are sensitive and reliable for the early identification of disease or, if untested for this purpose, are promising tools based on other variables. The survey responses for 16 measures/batteries are presented in brief in this review; full survey responses and summary tables are archived and publicly available on the Campaign to Prevent Alzheimer’s Disease by 2020 Web site (http://pad2020.org). A decision tree diagram highlighting critical decision points for selecting measures to meet varying clinical trials requirements has also been provided. Ultimately, the survey questionnaire, framework, and decision guidelines provided in this review should remain as useful aids for the evaluation of any new or updated sets of instruments in the years to come.
Cognition; Neuropsychological assessment; Alzheimer’s disease; Mild cognitive impairment; Clinical trials
This paper uses diffeomorphometry methods to quantify the order in which statistically significant morphometric change occurs in three medial temporal lobe regions, the amygdala, entorhinal cortex (ERC), and hippocampus among subjects with symptomatic and preclinical Alzheimer's disease (AD). Magnetic resonance imaging scans were examined in subjects who were cognitively normal at baseline, some of whom subsequently developed clinical symptoms of AD. The images were mapped to a common template, using shape-based diffeomorphometry. The multidimensional shape markers indexed through the temporal lobe structures were modeled using a changepoint model with explicit parameters, specifying the number of years preceding clinical symptom onset. Our model assumes that the atrophy rate of a considered brain structure increases years before detectable symptoms.
The results demonstrate that the atrophy changepoint in the ERC occurs first, indicating significant change 8–10 years prior to onset, followed by the hippocampus, 2–4 years prior to onset, followed by the amygdala, 3 years prior to onset. The ERC is significant bilaterally, in both our local and global measures, with estimates of ERC surface area loss of 2.4% (left side) and 1.6% (right side) annually. The same changepoint model for ERC volume gives 3.0% and 2.7% on the left and right sides, respectively. Understanding the order in which changes in the brain occur during preclinical AD may assist in the design of intervention trials aimed at slowing the evolution of the disease.
•We use diffeomorphometry to quantify the order in which statistically significant morphometric change occurs in three medial temporal lobe regions, the amygdala, entorhinal cortex (ERC), and hippocampus among subjects with symptomatic and preclinical Alzheimer's disease (AD).•We introduce a model on anatomical shape change in which changepoint is inferred, taking place some period of time before cognitive onset of AD.•The analysis uses a dataset arising from the BIOCARD study, in which all subjects were cognitively normal at baseline, some of whom subsequently developed clinical symptoms of AD.•The results demonstrate that the atrophy changepoint in the ERC occurs first, indicating significant change 8-10 years prior to onset, followed by hippocampus, 2-4 years prior to onset, followed by amygdala, 3 years prior to onset.•The ERC is significant bilaterally, in both our local and global measures, with estimates of ERC surface area loss of 2.4% (left side) and 1.6% (right side) annually.•Understanding the order in which changes in the brain occur during preclinical AD may assist in the design of intervention trials aimed at slowing the evolution of the disease.
AD, Alzheimer's disease; MCI, mild cognitive impairment; ERC, entorhinal cortex; NIH, Clinical Center of the National Institutes of Health; NIA, National Institute on Aging; NIMH, National Institute for Mental Health; GPB, Geriatric Psychiatry Branch; SPGR, spoiled gradient echo; CDR, clinical dementia rating; FWER, family-wise error rate; ROI-LDDMM, region-of-interest large deformation diffeomorphic metric mapping; RSS, residual sum of squares; MMSE, mini-mental state exam; diffeomorphometry, study of shape using a metric on the diffeomorphic connections between structures
Diffusion tensor imaging (DTI) is a promising method for identifying significant cross-sectional differences of white-matter tracts in normal controls (NC) and those with mild cognitive impairment (MCI) or Alzheimer’s disease (AD). There have not been many studies establishing its longitudinal utility.
Seventy-five participants (25 NC, 25 amnestic MCI, and 25 AD) had 3-Tesla MRI scans and clinical evaluations at baseline and 3, 6, and 12 months. Fractional anisotropy (FA) and mean diffusivity (MD) were analyzed at each time-point and longitudinally in eight a priori–selected areas taken from four regions of interest (ROIs).
Cross-sectionally, MD values were higher, and FA values lower in the fornix and splenium of the AD group compared with either MCI or NC (P < .01).Within-group change was more evident in MD than in FA over 12 months: MD increased in the inferior, anterior cingulum, and fornix in both the MCI and AD groups (P < .01).
There were stable, cross-sectional, region-specific differences between the NC and AD groups in both FA and MD at each time-point over 12 months. Longitudinally, MD was a better indicator of change than FA. Significant increases of fornix MD in the MCI group suggest this is an early indicator of progression.
Longitudinal; Alzheimer’s; disease; DTI; Anisotropy; Diffusivity
The clinical hallmark of Alzheimer’s disease (AD) is a gradual decline in cognitive function. For the majority of patients the initial symptom is an impairment in episodic memory, i.e., the ability to learn and retain new information. This is followed by impairments in other cognitive domains (e.g., executive function, language, spatial ability). This impairment in episodic memory is evident among individuals with mild cognitive impairment (MCI) and can be used to predict likelihood of progression to dementia, particularly in association with AD biomarkers. Additionally, cognitively normal individuals who are likely to progress to mild impairment tend to perform more poorly on tests of episodic memory than do those who remain stable. This cognitive presentation is consistent with the pathology of AD, showing neuronal loss in medial temporal lobe structures essential for normal memory. Similarly, there are correlations between MRI measures of medial temporal lobe structures and memory performance among individuals with MCI. There are recent reports that amyloid accumulation may also be associated with memory performance in cognitively normal individuals.
Alzheimer’s disease; dementia; cognition; cognitive testing; cognitive function; memory; biomarkers
The purpose of this paper is to investigate the relative utility of using neuroimaging, genetic, cerebrospinal fluid (CSF), and cognitive measures to predict progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia over a follow-up period. The studied subjects were 139 persons with MCI enrolled in the Alzheimer’s Disease Neuroimaging Initiative. Predictors of progression to AD included brain volume, ventricular volume, hippocampal volume, APOE ε4 two alleles, Aβ42, p-tau181, p-tau181/Aβ42, memory, language, and executive function. We employ a combination of Cox regression analyses and time-dependent receiver operating characteristic (ROC) methods to assess the prognostic utility and performance stability of candidate biomarkers. In a demographic-adjusted multivariable Cox model, seven measures— brain volume, hippocampal volume, ventricular volume, APOE ε4 two alleles, Aβ42, Memory composite, Executive function composite — predicted progression to AD. Time-dependent ROC revealed that this multivariable model had an area under the curve of 0.832, 0.788, 0.794, and 0.757 at 12, 18, 24, and 36 months respectively. Supplemental Cox models with time of origin set differentially at 12, 18, 24 and 36 months showed that six measures were significant predictors at 12 months whereas only memory and executive function predicted progression to AD at 18 and 24 months. The authors concluded that baseline volumetric MRI and cognitive measures selectively predict progression from MCI to AD, with cognitive measures remaining predictive even late in the follow-up period. These findings may inform case selection for AD clinical trials.
Alzheimer's disease; Cox models; Mild cognitive impairment; memory; ROC analysis
The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer’s disease patients and caregivers, health policy, economics, and the design of intervention studies.
To develop and validate a prediction algorithm that uses data from a single visit to estimate time to important disease endpoints for individual Alzheimer’s disease patients.
Two separate study cohorts (Predictors 1, N = 252; Predictors 2, N = 254), all initially with mild Alzheimer’s disease, were followed for 10 years at three research centers with semiannual assessments that included cognition, functional capacity, and medical, psychiatric and neurologic information. The prediction algorithm was based on a longitudinal Grade of Membership model developed using the complete series of semiannually-collected Predictors 1 data. The algorithm was validated on the Predictors 2 data using data only from the initial assessment to predict separate survival curves for three outcomes.
For each of the three outcome measures, the predicted survival curves fell well within the 95% confidence intervals of the observed survival curves. Patients were also divided into quintiles for each endpoint to assess the calibration of the algorithm for extreme patient profiles. In all cases, the actual and predicted survival curves were statistically equivalent. Predictive accuracy was maintained even when key baseline variables were excluded, demonstrating the high resilience of the algorithm to missing data.
The new prediction algorithm accurately predicts time to death, institutionalization, and need for full-time care in individual Alzheimer’s disease patients; it can be readily adapted to predict other important disease endpoints. The algorithm will serve an unmet clinical, research, and public health need.
Alzheimer’s disease; prediction algorithm; time to death; nursing home; full-time care; grade of membership model
We investigated a simple imaging sign for Alzheimer's disease (AD), using diffusion tensor imaging (DTI). We hypothesized that a reduction in fractional anisotropy (FA) in the fornix could be utilized as an imaging sign.
Twenty-three patients with AD, 24 patients with amnestic mild cognitive impairment (aMCI), and 25 control participants (NC) underwent DTI at baseline and one year later. The diagnosis was re-evaluated one year and three years after the initial scan. A color-scaled FA map was used to visually identify the FA reduction (“fornix sign”). We investigated whether the fornix sign could differentiate AD from NC, and could predict progression from aMCI to AD or NC to aMCI. We also quantified FA of the fornix to validate the fornix sign.
The fornix sign was identical to the lack of any voxels with an FA > 0.52 within the fornix. The fornix sign differentiated AD from NC with specificity of 1.0 and sensitivity of 0.56. It predicted conversion from NC to aMCI with specificity of 1.0 and sensitivity of 0.67, and from aMCI to AD with specificity of 0.94 and sensitivity of 0.83.
The fornix sign is a promising predictive imaging sign of AD.
fornix sign; fractional anisotropy; diffusion tensor imaging; Alzheimer's disease; mild cognitive impairment
This paper examines morphometry of MRI biomarkers derived from the network of temporal lobe structures including the amygdala, entorhinal cortex and hippocampus in subjects with preclinical Alzheimer's disease (AD). Based on template-centered population analysis, it is demonstrated that the structural markers of the amygdala, hippocampus and entorhinal cortex are statistically significantly different between controls and those with preclinical AD. Entorhinal cortex is the most strongly significant based on the linear effects model (p < .0001) for the high-dimensional vertex- and Laplacian-based markers corresponding to localized atrophy. The hippocampus also shows significant localized high-dimensional change (p < .0025) and the amygdala demonstrates more global change signaled by the strength of the low-dimensional volume markers. The analysis of the three structures also demonstrates that the volume measures are only weakly discriminating between preclinical and control groups, with the average atrophy rates of the volume of the entorhinal cortex higher than amygdala and hippocampus. The entorhinal cortex thickness also exhibits an atrophy rate nearly a factor of two higher in the ApoE4 positive group relative to the ApoE4 negative group providing weak discrimination between the two groups.
•We examine MRI measures in controls vs. subjects with ‘preclinical AD’.•Morphometry shape markers of the entorhinal cortex were most discriminating.•The mean atrophy rate of the entorhinal cortex exceeded the hippocampus or amygdala.
Previous studies have shown that high serum ceramides are associated with memory impairment and hippocampal volume loss, but have not examined dementia as an outcome. The aim of this study was to examine whether serum ceramides and sphingomyelins (SM) were associated with an increased risk of all-cause dementia and Alzheimer disease (AD).
Participants included 99 women without dementia aged 70–79, with baseline serum SM and ceramides, enrolled in a longitudinal population-based study and followed for up to 6 visits over 9 years. Baseline lipids, in tertiles, were examined in relation to all-cause dementia and AD using discrete time Cox proportional survival analysis. Lipids were analyzed using electrospray ionization tandem mass spectrometry.
Twenty-seven (27.3%) of the 99 women developed incident dementia. Of these, 18 (66.7%) were diagnosed with probable AD. Higher baseline serum ceramides, but not SM, were associated with an increased risk of AD; these relationships were stronger than with all-cause dementia. Compared to the lowest tertile, the middle and highest tertiles of ceramide d18:1–C16:0 were associated with a 10-fold (95% confidence interval [CI] 1.2–85.1) and 7.6-fold increased risk of AD (95% CI 0.9–62.1), respectively. The highest tertiles of ceramide d18:1–C24:0 (hazard ratio [HR] = 5.1, 95% CI 1.1–23.6) and lactosylceramide (HR = 9.8, 95% CI 1.2–80.1) were also associated with risk of AD. Total and high-density lipoprotein cholesterol and triglycerides were not associated with dementia or AD.
Results from this preliminary study suggest that particular species of serum ceramides are associated with incident AD and warrant continued examination in larger studies.
We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas–image misregistration, is used to capture the anatomical features of target images.
As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.
•A novel method to convert anatomical brain MRIs to feature vectors is introduced.•Degree of local atlas–image disagreement is used to capture the anatomical features.•The method was applied for pattern recognition of various neurodegenerative diseases.•The feature vectors agreed well with the known pathological hallmarks of diseases.•The method accurately categorized test images to the correct disease categories.
Atlas; Feature recognition; Alzheimer's disease; Huntington's disease; Primary progressive aphasia; Spinocerebellar ataxia
We previously established reliability and cross-sectional validity of the SIST-M (Structured Interview and Scoring Tool–Massachusetts Alzheimer's Disease Research Center), a shortened version of an instrument shown to predict progression to Alzheimer disease (AD), even among persons with very mild cognitive impairment (vMCI).
To test predictive validity of the SIST-M.
Participants were 342 community-dwelling, non-demented older adults in a longitudinal study. Baseline Clinical Dementia Rating (CDR) ratings were determined by either: 1) clinician interviews or 2) a previously developed computer algorithm based on 60 questions (of a possible 131) extracted from clinician interviews. We developed age+gender+education-adjusted Cox proportional hazards models using CDR-sum-of-boxes (CDR-SB) as the predictor, where CDR-SB was determined by either clinician interview or algorithm; models were run for the full sample (n=342) and among those jointly classified as vMCI using clinician- and algorithm-based CDR ratings (n=156). We directly compared predictive accuracy using time-dependent Receiver Operating Characteristic (ROC) curves.
AD hazard ratios (HRs) were similar for clinician-based and algorithm-based CDR-SB: for a 1-point increment in CDR-SB, respective HRs (95% CI)=3.1 (2.5,3.9) and 2.8 (2.2,3.5); among those with vMCI, respective HRs (95% CI) were 2.2 (1.6,3.2) and 2.1 (1.5,3.0). Similarly high predictive accuracy was achieved: the concordance probability (weighted average of the area-under-the-ROC curves) over follow-up was 0.78 vs. 0.76 using clinician-based vs. algorithm-based CDR-SB.
CDR scores based on items from this shortened interview had high predictive ability for AD – comparable to that using a lengthy clinical interview.
Alzheimer disease; mild cognitive impairment; dementia; CDR; instrument; questionnaire; validity; prediction; psychometric
This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.
Pancreatitis is a complex, progressively destructive inflammatory disorder. Alcohol was long thought to be the primary causative agent, but genetic contributions have been of interest since the discovery that rare PRSS1, CFTR, and SPINK1 variants were associated with pancreatitis risk. We now report two significant genome-wide associations identified and replicated at PRSS1-PRSS2 (1×10-12) and x-linked CLDN2 (p < 1×10-21) through a two-stage genome-wide study (Stage 1, 676 cases and 4507 controls; Stage 2, 910 cases and 4170 controls). The PRSS1 variant affects susceptibility by altering expression of the primary trypsinogen gene. The CLDN2 risk allele is associated with atypical localization of claudin-2 in pancreatic acinar cells. The homozygous (or hemizygous male) CLDN2 genotype confers the greatest risk, and its alleles interact with alcohol consumption to amplify risk. These results could partially explain the high frequency of alcohol-related pancreatitis in men – male hemizygous frequency is 0.26, female homozygote is 0.07.