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
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.
Better tools for assessing cognitive impairment in the early stages of Alzheimer’s disease (AD) are required to enable diagnosis of the disease before substantial neurodegeneration has taken place and to allow detection of subtle changes in the early stages of progression of the disease. The National Institute on Aging and the Alzheimer’s Association convened a meeting to discuss state of the art methods for cognitive assessment, including computerized batteries, as well as new approaches in the pipeline. Speakers described research using novel tests of object recognition, spatial navigation, attentional control, semantic memory, semantic interference, prospective memory, false memory and executive function as among the tools that could provide earlier identification of individuals with AD. In addition to early detection, there is a need for assessments that reflect real-world situations in order to better assess functional disability. It is especially important to develop assessment tools that are useful in ethnically, culturally and linguistically diverse populations as well as in individuals with neurodegenerative disease other than AD.
The fornix is the predominant outflow tract of the hippocampus, a brain region known to be affected early in the course of Alzheimer’s disease (AD). The aims of the present study were to: 1) examine the cross-sectional relationship between fornix DTI measurements (fractional anisotropy (FA), and mean (MD), axial (DA) and radial (DR) diffusivities), hippocampal volume, and memory performance, and 2) compare fornix DTI measures to hippocampal volumes as predictors of progression and transition from amnestic mild cognitive impairment (MCI) to AD dementia.
23 MCI participants with baseline hippocampal volumetry and diffusion tensor imaging received detailed evaluations at baseline, 3, 6, 12 months, and 2.5 years. Six participants converted to AD over the follow-up. Fornix and posterior cingulum DTI measurements and hippocampal volumes were ascertained using manual measures. Random effects models assessed each of the neuroimaging measures as predictors of decline on the MMSE, CDR-Sum of boxes and Memory z-scores; ROC analyses examined the predictive value for conversion to AD.
There was a significant correlation between fornix FA and hippocampal volumes. However, only the fornix measurements (FA, MD, DR, DA) were cross-sectionally correlated with memory z-scores. Both fornix FA and hippocampal volumes were predictive of memory decline. Individually, fornix FA and MD and hippocampal volumes were very good predictors of progression with likelihood ratios>83, and better than 90% accuracy.
Fornix FA both cross-sectionally correlated with and longitudinally predicted memory decline and progression to AD. Manually-drawn fornix ROI shows comparable promise to hippocampal volume as a predictive biomarker of progression and warrants replication in a larger study.
Fornix; Hippocampus; Mild Cognitive Impairment; Biomarker; Diffusion tensor imaging
Neuropsychiatric symptoms (NPS) occur frequently in mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). We examined the relationship between NPS and white matter integrity in these conditions. Twenty two individuals with MCI and 23 with mild AD underwent clinical assessments including the Neuropsychiatric Inventory Questionnaire and 3.0 Tesla magnetic resonance scans. Fractional anisotropy (FA) was measured in the following manually-drawn regions of interest (ROI): fornix, cingulum bundle, splenium, and cerebral peduncles (control region). The probability of having NPS by tertile of ROI FA was assessed using logistic regression. Because associations were similar within MCI and AD groups, the two groups were combined. Compared to those in the highest tertile, participants within the lowest anterior cingulum (AC) FA tertile were more likely to exhibit irritability, agitation, dysphoria, apathy, and nighttime behavioral disturbances (p<0.05). After adjusting for MMSE, participants in the lowest vs. highest tertile of AC FA were more likely to report irritability (OR: 7.21, p=0.041). Using DTI, low AC FA was associated with increased odds of irritability in mild AD and MCI participants. Further imaging studies are necessary to elucidate the role of the AC in the pathophysiology of NPS in AD and MCI.
Diffusion tensor imaging; Alzheimer’s disease; Mild cognitive impairment; Neuropsychiatric symptoms
To determine whether cerebrospinal fluid (CSF) biomarkers for Alzheimer disease fluctuate significantly over time in a cohort of older, mildly symptomatic individuals.
Biomarker validation in a clinical cohort.
University hospital inpatient unit.
Ten patients admitted for CSF drainage for diagnostic purposes.
Main Outcome Measures
The CSF levels of Aβ1–40, Aβ1–42, tau, and phosphorylated tau on threonine 181 (p-tau181) were measured every 6 hours for 24 or 36 hours.
The mean coefficient of variation values for each biomarker assessed in our 10 patients were 5.5% (95% CI, 3.8%–10.0%) for Aβ1–42, 12.2% (9.0%–24.2%) for Aβ1–40, 8.2% (5.7%–15.1%) for total tau, and 11.9% (8.5%–23.0%) for p-tau181. These values are only slightly higher than the variability in the assay. In addition, no significant circadian fluctuation in any Alzheimer disease biomarker was observed given the limitations of our sampling frequency.
In a cohort of elderly patients, little fluctuation in the levels of important Alzheimer disease biomarkers in lumbar CSF is seen as a function of time.
This study explores the longitudinal relationship between patient characteristics and use of four drug classes (antihypertensives, antidepressants, antipsychotics, and hormones) that showed significant changes in use rates over time in patients with Alzheimer’s disease (AD). Patient/caregiver-reported prescription medication usage was categorized by drug class for 201 patients from the Predictors Study. Patient characteristics included use of cholinesterase inhibitors and/or memantine, function, cognition, living situation, baseline age, and gender. Assessment interval, year of study entry, and site were controlled for. Before adjusting for covariates, use increased for antihypertensives (47.8% to 62.2%), antipsychotics (3.5% to 27.0%), and antidepressants (32.3% to 40.5%); use of hormones decreased (19.4% to 5.4%). After controlling for patient characteristics, effects of time on the use of antidepressants were no longer significant. Antihypertensive use was associated with poorer functioning, concurrent use of memantine, and older age. Antipsychotic use was associated with poorer functioning and poorer cognition. Antidepressant use was associated with younger age, poorer functioning, and concurrent use of cholinesterase inhibitors and memantine. Hormone use was associated with being female and younger age. Findings suggest accurate modeling of the AD treatment paradigm for certain subgroups of patients should include antihypertensives and antipsychotics in addition to cholinesterase inhibitors and memantine.
Alzheimer’s disease; antihypertensive; antidepressant; antipsychotic; hormone; longitudinal studies
The National Institute on Aging and the Alzheimer’s Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer’s disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings.
Mild cognitive impairment; AD dementia; Diagnosis
DTI is one of the most effective MR tools for the investigation of the brain anatomy. In addition to the gray matter, histopathological studies indicate that white matter is also a good target for both the early diagnosis of AD and for monitoring disease progression, which motivates us to use DTI to study AD patients in vivo. There are already a large amount of studies reporting significant differences between AD patients and controls, as well as to predict progression of disease in symptomatic non-demented individuals. Application of these findings in clinical practice remains to be demonstrated.
Alzheimer’s disease; mild cognitive impairment; white matter; diffusion tensor imaging; clinical application
The Clinical Dementia Rating (CDR) and CDR-Sum-of-Boxes (CDR-SB) can be utilized to grade mild but clinically important cognitive symptoms. However, sensitive clinical interview formats are lengthy.
To develop a brief instrument for obtaining CDR scores, and to assess its reliability and cross-sectional validity.
Using legacy data from expanded interviews conducted among 347 community-dwelling, older adults in a longitudinal study, we identified 60 questions about cognitive functioning in daily life–out of a possible 131– using clinical judgment, inter-item correlations, and principal components analysis. Items were selected in one cohort (n=147), and a computer algorithm for generating CDR scores was developed in this same cohort and re-run in a replication cohort (n=200) to evaluate how well the 60 items retained information from the original 131. Then, short interviews based on the 60 items were administered to 50 consecutively-recruited elders, with no or mild cognitive symptoms, at an Alzheimer Disease Research Center. CDR scores based on short interviews were compared with those from independent long interviews.
In the replication cohort, agreement between short and long CDR interviews ranged from κ =0.65–0.79, with κ =0.76 for Memory; κ =0.77 for global CDR; ICC (intra-class correlation coefficient) for CDR-SB=0.89. In the cross-sectional validation, short interview scores were slightly lower than those from long interviews, but good agreement was observed: κ ≥ 0.70 for global CDR and Memory; ICC for CDR-SB=0.73.
The SIST-M is a brief, reliable and sensitive instrument for obtaining CDR scores in persons with symptoms along the spectrum of mild cognitive change.
Alzheimer disease; mild cognitive impairment; Clinical Dementia Rating; instrument; questionnaire; clinical interview
This study examined in detail patterns of cholinesterase inhibitors (ChEIs) and memantine use and explored the relationship between patient characteristics and such use. Patients with probable Alzheimer disease AD (n = 201) were recruited from the Predictors Study in 3 academic AD centers and followed from early disease stages for up to 6 years. Random effects logistic regressions were used to examine effects of patient characteristics on ChEIs/memantine use over time. Independent variables included measures of function, cognition, comorbidities, the presence of extrapyramidal signs, psychotic symptoms, age, sex, and patient’s living situation at each interval. Control variables included assessment interval, year of study entry, and site. During a 6-year study period, rate of ChEIs use decreased (80.6% to 73.0%) whereas memantine use increased (2.0% to 45.9%). Random effects logistic regression analyses showed that ChEI use was associated with better function, no psychotic symptoms, and younger age. Memantine use was associated with better function, poorer cognition, living at home, later assessment interval, and later year of study entry. Results suggest that high rate of ChEI use and increasing memantine use over time are consistent with current practice guidelines of initiation of ChEIs in mild-to-moderate AD patients and initiation of memantine in moderate-to-severe patients.
Alzheimer disease; cholinesterase inhibitors; memantine; longitudinal studies
To examine the effect of specific “CSF profiles” on the rate of cognitive decline, disease progression, and risk of conversion to Alzheimer's disease (AD) dementia in patients with amnestic mild cognitive impairment (MCI).
Total tau (t-tau), tau phosphorylated at threonine 181 (p-tau181), and β-amyloid 1-42 peptide (Aβ42) were immunoassayed in CSF samples obtained from MCI patients enrolled in the Alzheimer's Disease Neuroimaging Initiative. Patients were then stratified by “CSF profiles”: (1) normal t-tau and Aβ42 levels (i.e., normal–t-tauAβ42), (2) normal t-tau but abnormal Aβ42 (i.e., abnormal–Aβ42), (3) abnormal t-tau but normal Aβ42 (i.e., abnormal–t-tau), and (4) abnormal t-tau and Aβ42 (i.e., abnormal–t-tauAβ42).
Fifty-eight sites in the US and Canada.
One hundred ninety-five MCI patients.
Main Outcome Measures
A composite cognitive measure, the CDR-Sum of Boxes, and conversion to AD.
MCI patients with a CSF profile of abnormal–Aβ42 or abnormal–t-tauAβ42 experienced a faster rate of decline on the composite cognitive measure and the CDR-Sum of Boxes compared to those with normal–t-tauAβ42. They also had a greater risk of converting to AD relative to the normal–t-tauAβ42 group. In contrast, those with a CSF profile of abnormal–t-tau did not differ from the normal–t-tauAβ42 group on any outcome. These findings were generally replicated when the sample was reclassified by patterns of p-tau181 and Aβ42 abnormalities.
β-amyloid abnormalities, but not tau alterations, are associated with cognitive deterioration, disease progression, and increased risk of conversion to AD dementia in patients with MCI. Patients with abnormal levels of Aβ42 may be prime targets for drug treatment and clinical trials in MCI.
CSF; MCI; cognitive decline; disease progression; conversion to AD
The ability to form associations between previously unrelated items of information, such as names and faces, is an essential aspect of episodic memory function. The neural substrate that determines success vs. failure in learning these associations remains to be elucidated. Using event-related functional MRI during the encoding of novel face-name associations, we found that successfully remembered face-name pairs showed significantly greater activation in the anterior hippocampal formation bilaterally and left inferior prefrontal cortex, compared to pairs that were forgotten. Functional connectivity analyses revealed significant correlated activity between the right and left hippocampus and neocortical regions during successful, but not attempted, encoding. These findings suggest that anterior regions of the hippocampal formation, in particular, are crucial for successful associative encoding and that the degree of coordination between hippocampal and neocortical activity may predict the likelihood of subsequent memory.