An exploration of cognitive subgroups in Alzheimer's disease

Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation

Background

Application of visual scoring scales for regional atrophy in Alzheimer's disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement.

Objective

To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI).

Methods

We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability.

Results

Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool.

Conclusions

Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

Exploring cognitive progression subtypes in the Framingham Heart Study

INTRODUCTION

Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes.

METHODS

Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains.

RESULTS

Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk.

DISCUSSION

This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention.

Highlights

We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.

The Media Coverage of Bruce Willis Reveals Unfamiliarity With Frontotemporal Degeneration

In 2022, Bruce Willis' family released a statement saying that he had been diagnosed with aphasia (an acquired language impairment) and would no longer be acting. Ten months later, the Willis family released another statement indicating that he received a more specific diagnosis of frontotemporal degeneration (FTD). This resulted in an explosion of media coverage, as prominent news outlets scrambled to produce stories describing FTD to a public largely unfamiliar with the disease. The quality of these stories varied widely, and in many cases the relationship between aphasia and FTD was misrepresented, as were basic descriptions and facts about FTD. FTD refers to a class of protein-misfolding diseases that are a common cause of aphasias due to neurodegeneration, or primary progressive aphasias (PPA). Rather than describing how FTD was discovered to be the underlying source of Mr. Willis' aphasia, many reports described his aphasia as "progressing into" FTD, implying they are two different disorders. Furthermore, these reports used the terminology of frontotemporal "dementia" rather than "degeneration", a term that invokes many stereotypes in the public imagination and may have contributed to misrepresentations in coverage. Instead of focusing on the language symptoms of PPA, reports often emphasized the personality and behavioral changes more closely associated with other variants of FTD. The substance of various facts, such as how common FTD is and how it can be treated, varied widely across reports. In sum, the media coverage of Mr. Willis' diagnosis reveals the extent to which the media and general public are uninformed about FTD and PPA. The remedy for this problem is to promote greater awareness of FTD, in both the public and the medical provider class. The Willis family's disclosure was a courageous act that helped bring much-needed attention to this disease.

Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling

Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

A latent class analysis of cognitive decline in US adults, BRFSS 2015-2020

BACKGROUND

Cognitive decline can be an early indicator for dementia. Using quantitative methods and national representative survey data, we can monitor the potential burden of disease at the population-level.

METHODS

BRFSS is an annual, nationally representative questionnaire in the United States. The optional cognitive decline module is a six-item self-reported scale pertaining to challenges in daily life due to memory loss and growing confusion over the past twelve months. Respondents are 45+, pooled from 2015-2020. Latent class analysis was used to determine unobserved subgroups of subjective cognitive decline (SCD) based on item response patterns. Multinomial logistic regression predicted latent class membership from socio-demographic covariates.

RESULTS

A total of 54,771 reported experiencing SCD. The optimal number of latent classes was three, labeled as Mild, Moderate, and Severe SCD. Thirty-five percent of the sample belonged to the Severe group. Members of this subgroup were significantly less likely to be older (65+ vs. 45-54 OR = 0.29, 95% CI: 0.23-0.35) and more likely to be non-Hispanic Black (OR = 1.80, 95% CI: 1.53-2.11), have not graduated high school (OR = 1.60, 95% CI: 1.34-1.91), or earned <$15K a year (OR = 3.03, 95% CI: 2.43-3.77).

CONCLUSIONS

This study determined three latent subgroups indicating severity of SCD and identified socio-demographic predictors. Using a single categorical indicator of SCD severity instead of six separate items improves the versatility of population-level surveillance.

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning

BACKGROUND

Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable.

METHODS

We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer's disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets.

RESULTS

We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42-73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters.

CONCLUSION

Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.

Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults

BACKGROUND

Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings.

OBJECTIVE

This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change.

METHODS

In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project.

RESULTS

A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines.

CONCLUSION

Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.

Heterogeneity and Factorial Structure in Alzheimer's Disease: A Cognitive Perspective

BACKGROUND

Alzheimer's disease (AD) patients show heterogeneous cognitive profiles which suggest the existence of cognitive subgroups. A deeper comprehension of this heterogeneity could contribute to move toward a precision medicine perspective.

OBJECTIVE

In this study, we aimed 1) to investigate AD cognitive heterogeneity as a product of the combination of within- (factors) and between-patients (sub-phenotypes) components, and 2) to promote its assessment in clinical practice by defining a small set of critical tests for this purpose.

METHODS

We performed factor mixture analysis (FMA) on neurocognitive assessment results of N = 230 patients with a clinical diagnosis of AD. This technique allowed to investigate the structure of cognitive heterogeneity in this sample and to characterize the core features of cognitive sub-phenotypes. Subsequently, we performed a tests selection based on logistic regression to highlight the best tests to detect AD patients in our sample. Finally, the accuracy of the same tests in the discrimination of sub-phenotypes was evaluated.

RESULTS

FMA revealed a structure characterized by five latent factors and four groups, which were identifiable by means of a few cognitive tests and were mainly characterized by memory deficits with visuospatial difficulties ("Visuospatial AD"), typical AD cognitive pattern ("Typical AD"), less impaired memory ("Mild AD"), and language/praxis deficits with relatively spared memory ("Nonamnestic AD").

CONCLUSION

The structure of cognitive heterogeneity in our sample of AD patients, as studied by FMA, could be summarized by four sub-phenotypes with distinct cognitive characteristics easily identifiable in clinical practice. Clinical implications under the precision medicine framework are discussed.

Different Cognitive Profiles Are Associated with Progression Rate and Age at Death in Probable Alzheimer's Disease

BACKGROUND

Cognitive profiles characterized by primarily language or visuospatial deficits have been documented in individuals meeting diagnostic criteria for probable Alzheimer's disease (AD), but their association with progression rate or overall survival is not well described.

OBJECTIVE

To compare time from diagnosis to severe disease stage and death in probable AD patients classified into three groups based on neuropsychological test performance: marked verbal impairment (Verb-PI) with relatively preserved visuospatial function, marked visuospatial impairment with preserved verbal function (Vis-PI), and balanced verbal and visuospatial impairments (Bal-PI).

METHODS

This prospective cohort study included 540 probable AD patients attending an academic memory clinic who were enrolled from 1995-2013 and followed annually. Eligible individuals had a Mini-Mental State Exam (MMSE) score ≥10 at baseline, and at least one annual follow up visit. We used Cox proportional hazards modeling to analyze the association of cognitive profiles with time to decline in MMSE and CDR Global Score.

RESULTS

Sixty-one (11.3%) individuals had a Verb-PI profile, 86 (16%) had a Vis-PI profile, and 393 (72.8%) a Bal-PI profile. MMSE decline to <10 was faster in Verb-PI than Vis-PI (HR 2.004, 95%CI, 1.062-3.780; p = 0.032). Progression to CDR-GS = 3 was faster in Verb-PI individuals compared to Bal-PI (HR 1.604, 95%CI, 1.022-2.515; p = 0.040) or Vis-PI (HR 2.388, 95%CI, 1.330-4.288; p = 0.004) individuals. Baseline cognitive profile did not affect mortality.

CONCLUSION

A recognition of different AD profiles may help to personalize care by providing a better understanding of pathogenesis and expected progression.

Associations Between Air Pollution Exposure and Empirically Derived Profiles of Cognitive Performance in Older Women

BACKGROUND

Elucidating associations between exposures to ambient air pollutants and profiles of cognitive performance may provide insight into neurotoxic effects on the aging brain.

OBJECTIVE

We examined associations between empirically derived profiles of cognitive performance and residential concentrations of particulate matter of aerodynamic diameter < 2.5 (PM2.5) and nitrogen dioxide (NO2) in older women.

METHOD

Women (N = 2,142) from the Women's Health Initiative Study of Cognitive Aging completed a neuropsychological assessment measuring attention, visuospatial, language, and episodic memory abilities. Average yearly concentrations of PM2.5 and NO2 were estimated at the participant's addresses for the 3 years prior to the assessment. Latent profile structural equation models identified subgroups of women exhibiting similar profiles across tests. Multinomial regressions examined associations between exposures and latent profile classification, controlling for covariates.

RESULT

Five latent profiles were identified: low performance across multiple domains (poor multi-domain; n = 282;13%), relatively poor verbal episodic memory (poor memory; n = 216; 10%), average performance across all domains (average multi-domain; n = 974; 45%), superior memory (n = 381; 18%), and superior attention (n = 332; 15%). Using women with average cognitive ability as the referent, higher PM2.5 (per interquartile range [IQR] = 3.64μg/m3) was associated with greater odds of being classified in the poor memory (OR = 1.29; 95% Confidence Interval [CI] = 1.10-1.52) or superior attention (OR = 1.30; 95% CI = 1.10-1.53) profiles. NO2 (per IQR = 9.86 ppb) was associated with higher odds of being classified in the poor memory (OR = 1.38; 95% CI = 1.17-1.63) and lower odds of being classified with superior memory (OR = 0.81; 95% CI = 0.67-0.97).

CONCLUSION

Exposure to PM2.5 and NO2 are associated with patterns of cognitive performance characterized by worse verbal episodic memory relative to performance in other domains.

APOE4 is associated with cognitive and pathological heterogeneity in patients with Alzheimer's disease: a systematic review

Possession of the ε4 allele of apolipoprotein E (APOE) is the primary genetic risk factor for the sporadic form of Alzheimer’s disease (AD). While researchers have extensively characterized the impact that APOE ε4 (APOE4) has on the susceptibility of AD, far fewer studies have investigated the phenotypic differences of patients with AD who are APOE4 carriers vs. those who are non-carriers. In order to understand these differences, we performed a qualitative systematic literature review of the reported cognitive and pathological differences between APOE4-positive (APOE4+) vs. APOE4-negative (APOE4−) AD patients. The studies performed on this topic to date suggest that APOE4 is not only an important mediator of AD susceptibility, but that it likely confers specific phenotypic heterogeneity in AD presentation, as well. Specifically, APOE4+ AD patients appear to possess more tau accumulation and brain atrophy in the medial temporal lobe, resulting in greater memory impairment, compared to APOE4− AD patients. On the other hand, APOE4− AD patients appear to possess more tau accumulation and brain atrophy in the frontal and parietal lobes, resulting in greater impairment in executive function, visuospatial abilities, and language, compared to APOE4+ AD patients. Although more work is necessary to validate and interrogate these findings, these initial observations of pathological and cognitive heterogeneity between APOE4+ vs. APOE4− AD patients suggest that there is a fundamental divergence in AD manifestation related to APOE genotype, which may have important implications in regard to the therapeutic treatment of these two patient populations.

Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy

The population of adults with Alzheimer’s disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, “Anosognosia dementia” and “Insightful dementia”, differentiate between severe participants based on clinical characteristics and biomarkers. The “Uncompensated mild cognitive impairment (MCI)” subtype, demonstrates clinical, demographic and imaging differences from the “Affective MCI” subtype. Differences were also observed between the “Worried Well” and “Healthy” clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.

Cognitive heterogeneity in probable Alzheimer disease: Clinical and neuropathologic features

OBJECTIVE

To identify heterogeneity in cognitive profiles of patients with probable Alzheimer disease (AD) who have mild to moderate dementia and satisfy inclusion and exclusion criteria for a typical AD clinical trial, and to determine whether cognitive profiles are systematically related to the clinical course and neuropathologic features of the disease.

METHODS

Neuropsychological test data from patients with mild to moderate probable AD (n = 4,711) were obtained from the National Alzheimer's Coordinating Center. Inclusion and exclusion criteria usually used in AD clinical trials were applied. Principal component analysis and model-based clustering were used to identify cognitive profiles in a subset of patients with autopsy-verified AD (n = 800) and validated in the overall (nonautopsy) sample and an independent cohort with similar test data. Relationships between cognitive profile, clinical characteristics, and rate of decline were examined with mixed-effects models.

RESULTS

In the autopsy-confirmed sample, 79.6% of patients had a typical AD cognitive profile (greater impairment of episodic memory than other cognitive functions), and 20.4% had an atypical profile (comparable impairment across cognitive domains). Similar results were obtained in the overall (typical 79.8%, atypical 20.2%) and validation (typical 71.8%, atypical 28.2%) samples. Atypicality was associated with younger age, male sex, lower probability of APOE ε4, less severe global dementia, higher depression scores, lower Braak stage at autopsy, and slower cognitive decline.

CONCLUSION

We can reliably identify distinct cognitive profiles among patients with clinically diagnosed probable AD that are associated with tangle pathology and with different rates of decline. This may have implications for clinical trials in AD, especially therapies targeting tau.

Subtypes Based on Neuropsychological Performance Predict Incident Dementia: Findings from the Rush Memory and Aging Project

BACKGROUND

In a previous report, we used latent class analysis (LCA) to identify natural subgroups of older adults in the Einstein Aging Study (EAS) based on neuropsychological performance. These subgroups differed in demographics, genetic profile, and prognosis. Herein, we assess the generalizability of these findings to an independent sample, the Rush Memory and Aging Project (MAP), which used an overlapping, but distinct neuropsychological battery.

OBJECTIVE

Our aim was to identify the association of natural subgroups based on neuropsychological performance in the MAP cohort with incident dementia and compare them with the associations identified in the EAS.

METHODS

MAP is a community-dwelling cohort of older adults living in the northeastern Illinois, Chicago. Latent class models were applied to baseline scores of 10 neuropsychological measures across 1,662 dementia-free MAP participants. Results were compared to prior findings from the EAS.

RESULTS

LCA resulted in a 5-class model: Mixed-Domain Impairment (n = 71, 4.3%), Memory-specific-Impairment (n = 274, 16.5%), Average (n = 767, 46.1%), Frontal Impairment (n = 222, 13.4%), and a class of Superior Cognition (n = 328, 19.7%). Similar to the EAS, the Mixed-Domain Impairment, the Memory-Specific Impairment, and the Frontal Impairment classes had higher risk of incident Alzheimer's disease when compared to the Average class. By contrast, the Superior Cognition had a lower risk of Alzheimer's disease when compared to the Average class.

CONCLUSIONS

Natural cognitive subgroups in MAP are similar to those identified in EAS. These similarities, despite study differences in geography, sampling strategy, and cognitive tests, suggest that LCA is capable of identifying classes that are not limited to a single sample or a set of cognitive tests.

Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study

OBJECTIVES

The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup's characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity.

METHODS

We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation.

RESULTS

The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes.

CONCLUSIONS

LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer's disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511-523).

Cognitive subtypes of probable Alzheimer's disease robustly identified in four cohorts

INTRODUCTION

Patients with Alzheimer's disease (AD) show heterogeneity in profile of cognitive impairment. We aimed to identify cognitive subtypes in four large AD cohorts using a data-driven clustering approach.

METHODS

We included probable AD dementia patients from the Amsterdam Dementia Cohort (n = 496), Alzheimer's Disease Neuroimaging Initiative (n = 376), German Dementia Competence Network (n = 521), and University of California, San Francisco (n = 589). Neuropsychological data were clustered using nonnegative matrix factorization. We explored clinical and neurobiological characteristics of identified clusters.

RESULTS

In each cohort, a two-clusters solution best fitted the data (cophenetic correlation >0.9): one cluster was memory-impaired and the other relatively memory spared. Pooled analyses showed that the memory-spared clusters (29%-52% of patients) were younger, more often apolipoprotein E (APOE) ɛ4 negative, and had more severe posterior atrophy compared with the memory-impaired clusters (all P < .05).

CONCLUSIONS

We could identify two robust cognitive clusters in four independent large cohorts with distinct clinical characteristics.

Latent Class Analysis of Neurodevelopmental Deficit After Exposure to Anesthesia in Early Childhood

INTRODUCTION

Although some studies have reported an association between early exposure to anesthesia and surgery and long-term neurodevelopmental deficit, the clinical phenotype of children exposed to anesthesia is still unknown.

METHODS

Data were obtained from the Western Australian Pregnancy Cohort Study (Raine) with neuropsychological tests at age 10 years measuring language, cognition, motor function, and behavior. Latent class analysis of the tests was used to divide the cohort into mutually exclusive subclasses of neurodevelopmental deficit. Multivariable polytomous logistic regression was used to evaluate the association between exposure to surgery and anesthesia and each latent class, adjusting for demographic and medical covariates.

RESULTS

In our cohort of 1444 children, latent class analysis identified 4 subclasses: (1) Normal: few deficits (n=1135, 78.6%); (2) Language and Cognitive deficits: primarily language, cognitive, and motor deficits (n=96, 6.6%); (3) Behavioral deficits: primarily behavioral deficits, (n=151, 10.5%); and (4) Severe deficits: deficits in all neuropsychological domains (n=62, 4.3%). Language and cognitive deficit group children were more likely to have exposure before age 3 (adjusted odds ratio [aOR], 2.11; 95% confidence interval [CI], 1.17-3.81), whereas a difference in exposure was not found between Behavioral or Severe deficit children (aOR, 1.00; 95% CI, 0.58-1.73, and aOR, 0.85; 95% CI, 0.34-2.15, respectively) and Normal children.

CONCLUSIONS

Our results suggest that in evaluating children exposed to surgery and anesthesia at an early age, the phenotype of interest may be children with deficits primarily in language and cognition, and not children with broad neurodevelopmental delay or primarily behavioral deficits.

Distinct subtypes of Alzheimer's disease based on patterns of brain atrophy: longitudinal trajectories and clinical applications

Atrophy patterns on MRI can reliably predict three neuropathological subtypes of Alzheimer’s disease (AD): typical, limbic-predominant, or hippocampal-sparing. A method to enable their investigation in the clinical routine is still lacking. We aimed to (1) validate the combined use of visual rating scales for identification of AD subtypes; (2) characterise these subtypes at baseline and over two years; and (3) investigate how atrophy patterns and non-memory cognitive domains contribute to memory impairment. AD patients were classified as either typical AD (n = 100), limbic-predominant (n = 33), or hippocampal-sparing (n = 35) by using the Scheltens’ scale for medial temporal lobe atrophy (MTA), the Koedam’s scale for posterior atrophy (PA), and the Pasquier’s global cortical atrophy scale for frontal atrophy (GCA-F). A fourth group with no atrophy was also identified (n = 30). 230 healthy controls were also included. There was great overlap among subtypes in demographic, clinical, and cognitive variables. Memory performance was more dependent on non-memory cognitive functions in hippocampal-sparing and the no atrophy group. Hippocampal-sparing and the no atrophy group showed less aggressive disease progression. Visual rating scales can be used to identify distinct AD subtypes. Recognizing AD heterogeneity is important and visual rating scales may facilitate investigation of AD heterogeneity in clinical routine.

Dissociating Statistically-Determined Alzheimer's Disease/Vascular Dementia Neuropsychological Syndromes Using White and Gray Neuroradiological Parameters

BACKGROUND

There is remarkable heterogeneity in clinical Alzheimer's disease (AD) or vascular dementia (VaD).

OBJECTIVES

1) To statistically examine neuropsychological data to determine dementia subgroups for individuals clinically diagnosed with AD or VaD and then 2) examine group differences in specific gray/white matter regions of interest.

METHODS

A k-means cluster analysis requested a 3-group solution from neuropsychological data acquired from individuals diagnosed clinically with AD/VaD. MRI measures of hippocampal, caudate, ventricular, subcortical lacunar infarction, whole brain volume, and leukoaraiosis (LA) were analyzed. Three regions of LA volumes were quantified and these included the periventricular (5 mm around the ventricles), infracortical (5 mm beneath the gray matter), and deep (between periventricular and infracortical) regions.

RESULTS

Cluster analysis sorted AD/VaD patients into single domain amnestic (n = 41), single-domain dysexecutive (n = 26), and multi-domain (n = 26) phenotypes. Multi-domain patients exhibited worst performance on language tests; however, multi-domain patients were equally impaired on memory tests when compared to amnestic patients. Statistically-determined groups dissociated using neuroradiological parameters: amnestic and multi-domain groups presented with smaller hippocampal volume while the dysexecutive group presented with greater deep, periventricular, and whole brain LA. Neither caudate nor lacunae volume differed by group. Caudate nucleus volume negatively correlated with total LA in the dysexecutive and multi-domain groups.

CONCLUSIONS

There are at least three distinct subtypes embedded within patients diagnosed clinically with AD/VaD spectrum dementia. We encourage future research to assess a) the neuroradiological substrates underlying statistically-determined AD/VaD spectrum dementia and b) how statistical modeling can be integrated into existing diagnostic criteria.

The identification of cognitive subtypes in Alzheimer's disease dementia using latent class analysis

OBJECTIVE

Alzheimer's disease (AD) is a heterogeneous disorder with complex underlying neuropathology that is still not completely understood. For better understanding of this heterogeneity, we aimed to identify cognitive subtypes using latent class analysis (LCA) in a large sample of patients with AD dementia. In addition, we explored the relationship between the identified cognitive subtypes, and their demographical and neurobiological characteristics.

METHODS

We performed LCA based on neuropsychological test results of 938 consecutive probable patients with AD dementia using Mini-Mental State Examination as the covariate. Subsequently, we performed multinomial logistic regression analysis with cluster membership as dependent variable and dichotomised demographics, APOE genotype, cerebrospinal fluid biomarkers and MRI characteristics as independent variables.

RESULTS

LCA revealed eight clusters characterised by distinct cognitive profile and disease severity. Memory-impaired clusters-mild-memory (MILD-MEM) and moderate-memory (MOD-MEM)-included 43% of patients. Memory-spared clusters mild-visuospatial-language (MILD-VILA), mild-executive (MILD-EXE) and moderate-visuospatial (MOD-VISP) -included 29% of patients. Memory-indifferent clusters mild-diffuse (MILD-DIFF), moderate-language (MOD-LAN) and severe-diffuse (SEV-DIFF) -included 28% of patients. Cognitive clusters were associated with distinct demographical and neurobiological characteristics. In particular, the memory-spared MOD-VISP cluster was associated with younger age, APOE e4 negative genotype and prominent atrophy of the posterior cortex.

CONCLUSIONS

Using LCA, we identified eight distinct cognitive subtypes in a large sample of patients with AD dementia. Cognitive clusters were associated with distinct demographical and neurobiological characteristics.

Do patients with young onset Alzheimer's disease deteriorate faster than those with late onset Alzheimer's disease? A review of the literature

BACKGROUND

Young onset Alzheimer's disease (YOAD; onset before 65 years of age) is thought to have a more rapid course and increased rate of progression compared to late onset Alzheimer's disease (LOAD). This assumption appears partly due to important clinical, structural, neuropathological, and neurochemical differences suggesting YOAD is a separate entity to LOAD. The aim in this review was to systematically identify and examine appropriate studies comparing rate of cognitive decline between patients with YOAD and patients with LOAD.

METHODS

A computer-based literature search was initially undertaken, followed by citation tracking and search of related papers. Primary research studies specifically focused on the rate of cognitive decline between people with YOAD and LOAD were included. Studies were described, critically analyzed, presented, and discussed in the review.

RESULTS

Four studies were included, of which three were longitudinal and one was a case-control study. Three of the included studies found a faster rate of decline in patients with YOAD, and one found no difference in rate of decline between the two groups.

CONCLUSIONS

The findings of the review are mixed and conflicting, and limited by the heterogeneity of the included studies. There is a need for future research to design systematic studies that include sufficient sample sizes and follow-up periods, and control for possible confounding factors such as education level, baseline cognitive impairment, and vascular risk factors. This will help to validate the findings so far and improve our understanding of the rate of cognitive decline in people with YOAD and LOAD.

Associative memory and its cerebral correlates in Alzheimer׳s disease: evidence for distinct deficits of relational and conjunctive memory

This study investigated the impact of Alzheimer׳s disease (AD) on conjunctive and relational binding in episodic memory. Mild AD patients and controls had to remember item-color associations by imagining color either as a contextual association (relational memory) or as a feature of the item to be encoded (conjunctive memory). Patients׳ performance in each condition was correlated with cerebral metabolism measured by FDG-PET. The results showed that AD patients had an impaired capacity to remember item-color associations, with deficits in both relational and conjunctive memory. However, performance in the two kinds of associative memory varied independently across patients. Partial Least Square analyses revealed that poor conjunctive memory was related to hypometabolism in an anterior temporal-posterior fusiform brain network, whereas relational memory correlated with metabolism in regions of the default mode network. These findings support the hypothesis of distinct neural systems specialized in different types of associative memory and point to heterogeneous profiles of memory alteration in Alzheimer׳s disease as a function of damage to the respective neural networks.

Three-question dementia screening. Development of the Salzburg Dementia Test Prediction (SDTP)

BACKGROUND

To date, short dementia screenings are often limited by poor specificity or still take too much time with respect to the restricted resources of primary care physicians and the increasing number of dementia disorders. As a new instrument, the three-question dementia screening (SDTP, Salzburg Dementia Test Prediction) should be compared with the eight-item screening of Chen et al. and the CERAD battery (Consortium to Establish a Registry for Alzheimer's Disease), focusing on specificity and economy of time.

MATERIALS AND METHODS

We tested 404 patients (243 women). The mean age of the subjects was 80.1 years (SD = 6.8) for men and 83.2 years (SD = 6.0) for women. The mean Mini-Mental State Examination (MMSE) score was 21.9 (SD =  5.8) for men and 21.1 (SD =  6.3) for women. Artificial neural networks (ANNs) were used to find a mathematical model that allows the total MMSE to be predicted with only three questions of the MMSE. This is achieved by multiplying the outcome of the three best predictor questions with a weighting coefficient, which was delineated by using ANNs.

RESULTS

The Salzburg Dementia Test Prediction (SDTP) had a sensitivity of 94% (95% CI: 87-97%) for screening of possible dementia, when the MMSE (MMSE < 25/30) was used as the reference test method and 96% when the CERAD was used. The specificity was 68% (95% CI: 57-77%) if the MMSE was used and 70% if the whole test battery (CERAD) was used, which is as sensitive as and more specific than the eight-item screening.

CONCLUSION

The SDTP is a time-saving instrument for screening of dementia, which is as sensitive as and more specific than the eight-item screening of Chen et al. and provides a prediction of the MMSE with high accuracy.

Insights into spared memory capacity in amnestic MCI and Alzheimer's Disease via minimal interference

Impairment on standard tests of delayed recall is often already maximal in the aMCI stage of Alzheimer's Disease. Neuropathological work shows that the neural substrates of memory function continue to deteriorate throughout the progression of the disease, hinting that further changes in memory performance could be tracked by a more sensitive test of delayed recall. Recent work shows that retention in aMCI patients can be raised well above floor when the delay period is devoid of further material - 'Minimal Interference'. This memory enhancement is thought to be the result of improved memory consolidation. Here we used the minimal interference/interference paradigm (word list retention following 10 min of quiet resting vs. picture naming) in a group of 17 AD patients, 25 aMCI patients and 25 controls. We found (1) that retention can be improved significantly by minimal interference in patients with aMCI and patients with mild to moderate AD; (2) that the minimal interference paradigm is sensitive to decline in memory function with disease severity, even when performance on standard tests has reached floor; and (3) that this paradigm can differentiate well (80% sensitivity and 100% specificity) between aMCI patients who progress and do not progress to AD within 2 years. Our findings support the notion that the early memory dysfunction in AD is associated with an increased susceptibility to memory interference and are suggestive of a gradual decline in consolidation capacity with disease progression.