Cognitive heterogeneity in probable Alzheimer disease: Clinical and neuropathologic features

Influence of Different Diagnostic Criteria on Alzheimer Disease Clinical Research

BACKGROUND AND OBJECTIVES

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals.

METHODS

We applied clinical and biomarker data in algorithms to classify individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort according to the following diagnostic guidelines for AD: 2011 NIA-AA, 2016 IWG-2, 2018 NIA-AA, and 2021 IWG-3, assigning the following generic diagnostic labels: (1) not AD (nAD), (2) increased risk of developing AD (irAD), and (3) AD. Diagnostic labels were compared according to their frequency, convergence across guidelines, biomarker profiles, and prognostic value. We also evaluated the diagnostic discordance among the criteria.

RESULTS

A total of 1,195 individuals (mean age 73.2 ± 7.2 years, mean education 16.1 ± 2.7, 44.0% female) presented different repartitions of diagnostic labels according to the 2011 NIA-AA (nAD = 37.8%, irAD = 23.0%, AD = 39.2%), 2016 IWG-2 (nAD = 37.7%, irAD = 28.7%, AD = 33.6%), 2018 NIA-AA (nAD = 40.7%, irAD = 9.3%, AD = 50.0%), and 2021 IWG-3 (nAD = 51.2%, irAD = 8.4%, AD = 48.3%) frameworks. Discordant diagnoses across all guidelines were found in 512 participants (42.8%) (138 [91.4%] occurring in only β-amyloid [CU 65.4%, CI 34.6%] and 191 [78.6%] in only tau-positive [CU 71.7%, CI 28.3%] individuals). Differences in predicting cognitive impairment between nAD and irAD groups were observed with the 2011 NIA-AA (hazard ratio [HR] 2.21, 95% CI 1.34-3.65, p = 0.002), 2016 IWG-2 (HR 2.81, 95% CI 1.59-4.96, p < 0.000), and 2021 IWG-3 (HR 3.61, 95% CI 2.09-6.23, p < 0.000), but not with 2018 NIA-AA (HR 1.69, 95% CI 0.87-3.28, p = 0.115).

DISCUSSION

Over 42% of the studied population presented discordant diagnoses when using the different examined AD criteria, mostly in individuals with a single positive biomarker. Except for 2018 NIA-AA, all guidelines identified asymptomatic individuals at risk of cognitive impairment. Our findings highlight important differences between the guidelines, emphasizing the necessity for updated criteria with enhanced staging metrics, considering clinical, research, therapeutic, and trial design aspects.

Efficacy of selective serotonin reuptake inhibitors-related antidepressants in Alzheimer's disease: a meta-analysis

OBJECTIVE

To study the effects of selective serotonin reuptake inhibitors (SSRIs) on cognitive functions, mental improvements, and adverse effects in patients with Alzheimer's disease (AD).

METHODS

Registered in INPLASY (INPLASY202450004), five drugs (citalopram, s-citalopram, quetiapine, olanzapine, and sertraline) were selected as representatives. A comprehensive search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library up to May 15, 2024. Search terms were combined using Boolean operators, specifically 'AND' between different categories (e.g., 'Alzheimer's Disease' AND 'SSRIs') and 'OR' within the same category (e.g., 'citalopram OR s-citalopram OR quetiapine OR olanzapine OR sertraline'), to ensure a thorough retrieval of relevant studies. The selection followed rigorous inclusion and exclusion criteria for meta-analysis.

RESULTS

Fourteen articles from 1118 were selected for meta-analysis. The indicators, including Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Brief Psychiatric Rating Scale (BPRS), and Cornell Scale for Depression in Dementia (CSDD), were used to assess the effects of the drugs on AD treatment. According to the results of NPI, CSDD, BPRS, MMSE, and security assessments, the five antidepressants have significant advantages in AD treatment compared with placebo, while the MMSE of the patient treated with the antidepressants did not show notable changes compared with patients treated only with placebo. Statistical analyses were conducted using Review Manager 5.3, employing random-effects models to account for study heterogeneity and sensitivity analyses to test the robustness of our findings.

CONCLUSION

This study suggests that SSRI-related antidepressants have great potential values in AD treatment, and further research on the application of SSRI-related antidepressants in AD treatment is necessary.

Challenges and prospects in advancing clinical neuropsychology

In the ever-evolving field of clinical neuropsychology, significant methodological and conceptual limitations hinder progress. To ensure the continued relevance of this discipline amidst remarkable advancements in neuroscience, medicine, and methodology, these obstacles must be addressed. This opinion article identifies inherent limitations within current clinical neuropsychology, including issues such as multi-collinearity in neuropsychological assessments, lack of validated tools reflecting contemporary cognitive function models, and the use of divergent theoretical frameworks in evaluations, leading to a gap between theory and practice. The disconnect between behavior and biomarkers, particularly evident in neurodegenerative diseases but also relevant for other pathologies, together with the rise of genetic analyses, necessitate change. Methodological improvements are crucial for ensuring the discipline's future relevance. Looking ahead, key perspectives and challenges are outlined, emphasizing the need for a holistic approach to cognitive functioning and congruent tools, patient engagement in experimental studies, rectification of biases, and exploration of variables like personality. Training professionals to bridge the gap between practice and research is essential. By addressing these challenges, clinical neuropsychology can not only adapt to the evolving landscape but also shape it, ensuring a brighter future for the field.

Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach

Introduction

Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown.

Methods

Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes.

Results

We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8).

Discussion

Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Classification Of MeMory InTerventions: Rationale and developmental process of the COMMIT tool

ABSTRACTOver the last decades, numerous memory interventions have been developed to mitigate memory decline in normal ageing. However, there is a large variability in the success of memory interventions, and it remains poorly understood which memory intervention programs are most effective and for whom. This is partially explained by the heterogeneity of memory intervention protocols across studies as well as often poor reporting of the study design. To facilitate a reporting framework that enables researchers to systemize the content and design of memory intervention paradigms, we developed the Classification Of MeMory InTerventions (COMMIT) tool using a 3-stage developmental process. Briefly, COMMIT was based on qualitative content analysis of already existing memory intervention studies published between April 1983 and July 2020, and iteratively validated by both internal and external expert panels. COMMIT provides an easily-applicable interactive tool that enables systematic description of memory intervention studies, together with instructions on how to use this classification tool. Our main goal is to provide a tool that enables the reporting and classification of memory interventions in a transparent, comprehensible, and complete manner, to ensure a better comparability between memory interventions, and, to ultimately contribute to the question which memory intervention shows the greatest benefits.

Controversial Past, Splendid Present, Unpredictable Future: A Brief Review of Alzheimer Disease History

Background: The concept of Alzheimer disease (AD)-since its histological discovery by Alzheimer to the present day-has undergone substantial modifications. Methods: We conducted a classical narrative review of this field with a bibliography selection (giving preference to Medline best match). Results: The following subjects are reviewed and discussed: Alzheimer's discovery, Kraepelin's creation of a new disease that was a rare condition until the 1970's, the growing interest and investment in AD as a major killer in a society with a large elderly population in the second half of the 20th century, the consolidation of the AD clinicopathological model, and the modern AD nosology based on the dominant amyloid hypothesis among many others. In the 21st century, the development of AD biomarkers has supported a novel biological definition of AD, although the proposed therapies have failed to cure this disease. The incidence of dementia/AD has shown a decrease in affluent countries (possibly due to control of risk factors), and mixed dementia has been established as the most frequent etiology in the oldest old. Conclusions: The current concept of AD lacks unanimity. Many hypotheses attempt to explain its complex physiopathology entwined with aging, and the dominant amyloid cascade has yielded poor therapeutic results. The reduction in the incidence of dementia/AD appears promising but it should be confirmed in the future. A reevaluation of the AD concept is also necessary.

Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles

INTRODUCTION

Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity.

METHODS

We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module-based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data.

RESULTS

Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions.

DISCUSSION

Our findings demonstrate that polygenic profiling based on co-expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD.

HIGHLIGHTS

Co-expression gene-network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity. AD-associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles. Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups). Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional-specific brain atrophy data.

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 novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Background

Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.

Objective

This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.

Methods

First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.

Results

Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.

Conclusion

These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

A biomarker-validated time scale in years of disease progression has identified early- and late-onset subgroups in sporadic Alzheimer's disease

BACKGROUND

It is possible to calculate the number of years to the expected clinical onset (YECO) of autosomal-dominant Alzheimer's disease (adAD). A similar time scale is lacking for sporadic Alzheimer's disease (sAD). The purpose was to design and validate a time scale in YECO for patients with sAD in relation to CSF and PET biomarkers.

METHODS

Patients diagnosed with Alzheimer's disease (AD, n = 48) or mild cognitive impairment (MCI, n = 46) participated in the study. They underwent a standardized clinical examination at the Memory clinic, Karolinska University Hospital, Stockholm, Sweden, which included present and previous medical history, laboratory screening, cognitive assessment, CSF biomarkers (Aβ₄₂, total-tau, and p-tau), and an MRI of the brain. They were also assessed with two PET tracers, ¹¹C-Pittsburgh compound B and ¹⁸F-fluorodeoxyglucose. Assuming concordance of cognitive decline in sAD and adAD, YECO for these patients was calculated using equations for the relationship between cognitive performance, YECO, and years of education in adAD (Almkvist et al. J Int Neuropsychol Soc 23:195-203, 2017).

RESULTS

The mean current point of disease progression was 3.2 years after the estimated clinical onset in patients with sAD and 3.4 years prior to the estimated clinical onset in patients with MCI, as indicated by the median YECO from five cognitive tests. The associations between YECO and biomarkers were significant, while those between chronological age and biomarkers were nonsignificant. The estimated disease onset (chronological age minus YECO) followed a bimodal distribution with frequency maxima before (early-onset) and after (late-onset) 65 years of age. The early- and late-onset subgroups differed significantly in biomarkers and cognition, but after control for YECO, this difference disappeared for all except the APOE e4 gene (more frequent in early- than in late-onset).

CONCLUSIONS

A novel time scale in years of disease progression based on cognition was designed and validated in patients with AD using CSF and PET biomarkers. Two early- and late-disease onset subgroups were identified differing with respect to APOE e4.

Sex and gender differences in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis: a narrative review

Neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), exhibit high phenotypic variability and they are very common in the general population. These diseases are associated with poor prognosis and a significant burden on patients and their caregivers. Although increasing evidence suggests that biological sex is an important factor for the development and phenotypical expression of some NDs, the role of sex and gender in the diagnosis and prognosis of NDs has been poorly explored. Current knowledge relating to sex- and gender-related differences in the epidemiology, clinical features, biomarkers, and treatment of AD, PD, and ALS will be summarized in this narrative review. The cumulative evidence hitherto collected suggests that sex and gender are factors to be considered in explaining the heterogeneity of these NDs. Clarifying the role of sex and gender in AD, PD, and ALS is a key topic in precision medicine, which will facilitate sex-specific prevention and treatment strategies to be implemented in the near future.

Signatures of Neuropsychological Test Results in the Long Life Family Study: A Cluster Analysis

BACKGROUND

Discovering patterns of cognitive domains and characterizing how these patterns associate with other risk factors and biomarkers can improve our understanding of the determinants of cognitive aging.

OBJECTIVE

To discover patterns of cognitive domains using neuropsychological test results in Long Life Family Study (LLFS) and characterize how these patterns associate with aging markers.

METHODS

5,086 LLFS participants were administered neuropsychological tests at enrollment. We performed a cluster analysis of six baseline neuropsychological test scores and tested the association between the identified clusters and various clinical variables, biomarkers, and polygenic risk scores using generalized estimating equations and the Chi-square test. We used Cox regression to correlate the clusters with the hazard of various medical events. We investigated whether the cluster information could enhance the prediction of cognitive decline using Bayesian beta regression.

RESULTS

We identified 12 clusters with different cognitive signatures that represent profiles of performance across multiple neuropsychological tests. These signatures significantly correlated with 26 variables including polygenic risk scores, physical and pulmonary functions, and blood biomarkers and were associated with the hazard of mortality (p < 0.01), cardiovascular disease (p = 0.03), dementia (p = 0.01), and skin cancer (p = 0.03).

CONCLUSION

The identified cognitive signatures capture multiple domains simultaneously and provide a holistic vision of cognitive function, showing that different patterns of cognitive function can coexist in aging individuals. Such patterns can be used for clinical intervention and primary care.

Transcranial Direct Current Stimulation (tDCS) as a Useful Rehabilitation Strategy to Improve Cognition in Patients With Alzheimer's Disease and Parkinson's Disease: An Updated Systematic Review of Randomized Controlled Trials

Alzheimer's disease (AD) and Parkinson's disease (PD) are neurodegenerative disorders characterized by cognitive impairment and functional decline increasing with disease progression. Within non-pharmacological interventions, transcranial direct current stimulation (tDCS) might represent a cost-effective rehabilitation strategy to implement cognitive abilities with positive implications for functional autonomy and quality-of-life of patients. Our systematic review aimed at evaluating the effects of tDCS upon cognition in people suffering from AD and PD. We searched for randomized controlled trials (RCTs) into PubMed, Web of Science, and Cochrane Library. Three review authors extracted data of interest, with neuropsychological tests or experimental cognitive tasks scores as outcome measures. A total of 17 RCTs (10 trials for AD and 7 trials for PD) were included. Compared with sham stimulation, tDCS may improve global cognition and recognition memory in patients with AD and also some executive functions (i.e., divided attention, verbal fluency, and reduction of sensitivity to interference) in patients with PD. Criticism remains about benefits for the other investigated cognitive domains. Despite preliminary emerging evidences, larger RCTs with common neuropsychological measures and long-term follow-ups establishing longevity of the observed effects are necessary for future research in applied psychology field, alongside improved clinical guidelines on the neurodegenerative disorders pertaining electrodes montage, sessions number, duration and intensity of the stimulation, and cognitive battery to be used.

Evaluating a Memory Clinic Using the RE-AIM Model. The Experience of the "Memory and Neuropsychiatry Clinic" in Hospital Del Salvador, Chile

The development of healthcare services for dementia is key to improving access to care and post-diagnostic support for people living with dementia. Memory Units have emerged as a new healthcare service composed of multidisciplinary teams with the goal of improving diagnosis and/or management of dementia patients. The main objective of this study was to describe and evaluate the Reach and Effectiveness of a Memory Unit in a public hospital in Chile, using the RE-AIM model, a multi-component model that allows for the evaluation of the implementation of ongoing healthcare programs. Regarding “R” (Reach): from March 2018 up to June 2019, a total of 510 patients were referred and assessed. Most patients came from primary care (51.9%) and from outpatient services at the Hospital Salvador (39.2%), particularly from the Neurology (63.3%) and Psychiatry (16.0%) departments. We estimated that our Memory Unit assessed 5.39% of all of the dementia patients living in the area of referral. With respect to “E” (Effectiveness): 419 patients are still being followed up at the Memory Unit. Ninety-one patients (18%) were discharged. Of these, 55 (66%) were referred to primary healthcare, 28 (31%) to other outpatient services, 9 (10%) to a specialized mental healthcare center, and 9 (10%) to a daycare center. Due to the short period of time that the Memory Unit has been operating, no other RE-AIM dimensions could be evaluated at this juncture. To our knowledge, this is the first implementation study of a Memory Unit in Latin America, and the first using the RE-AIM model. Although cultural differences worldwide might play a role in the lack of international guidelines, the publication of the experience of the first year of this unit in Chile could inform new countries about this process. Ongoing challenges include continuing to collect data to complement the RE-AIM evaluation and developing a protocol that can be adopted elsewhere in Chile and Latin America. Further studies are needed to assess the benefits of a Memory Unit in comparison to regular care and to develop a model that assures continuity and coordination of care for people with dementia.

Tau Acetylation in Entorhinal Cortex Induces its Chronic Hippocampal Propagation and Cognitive Deficits in Mice

BACKGROUND

Increased tau acetylation at K174, K274, K280, and K281 has been observed in the brains of Alzheimer's disease (AD) patients or in transgenic mice, but the role of acetylation in tau propagation is elusive.

OBJECTIVE

To study the effect of tau acetylation in entorhinal cortex on tau transmission and learning and memory.

METHODS

Stereotactic brain injection, behavioral test, electrophysiological recording, immunohistochemistry, and immunofluorescence were used.

RESULTS

We constructed the hyperacetylation mimics of tau (AAV-Tau-4Q), the non-acetylation tau mutant (AAV-Tau-4R), and the wild-type tau (AAV-Tau-WT). By overexpressing these different tau proteins in the entorhinal cortex (EC) of 2-month-old mice, we found that overexpressing Tau-4Q in EC for 3 or 6 months (to 5 or 8 months of age) neither induces tau propagation to dentate gyrus (DG) nor glial activation in DG, nor spatial memory deficit. However, overexpressing Tau-WT and Tau-4Q in EC for 13.5 months (15.5 months of age) at 2 months promoted tau propagation respectively to granulosa and hilus of DG with glial activation, synaptic dysfunction, and memory deficit, while overexpressing Tau-4R abolished tau propagation with improved cellular pathologies and cognitive functions. Furthermore, overexpressing Tau-4Q in unilateral DG of 2-month-old mice for 8 weeks also promoted its contralateral transmission with glial activation, and mice with tau (Tau-WT, Tau-4Q, and Tau-4R) overexpression in DG showed cognitive deficits compared with the empty vector controls.

CONCLUSION

Tau acetylation induces a time-dependent propagation from EC to DG, and only hippocampus but not EC tau accumulation induces cognitive deficits.

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.

Therapy for Alzheimer's disease: Missing targets and functional markers?

The development of the next generation therapy for Alzheimer's disease (AD) presents a huge challenge given the number of promising treatment candidates that failed in trials, despite recent advancements in understanding of genetic, pathophysiologic and clinical characteristics of the disease. This review reflects some of the most current concepts and controversies in developing disease-modifying and new symptomatic treatments. It elaborates on recent changes in the AD research strategy for broadening drug targets, and potentials of emerging non-pharmacological treatment interventions. Established and novel biomarkers are discussed, including emerging cerebrospinal fluid and plasma biomarkers reflecting tau pathology, neuroinflammation and neurodegeneration. These fluid biomarkers together with neuroimaging findings can provide innovative objective assessments of subtle changes in brain reflecting disease progression. A particular emphasis is given to neurophysiological biomarkers which are well-suited for evaluating the brain overall neural network integrity and function. Combination of multiple biomarkers, including target engagement and outcome biomarkers will empower translational studies and facilitate successful development of effective therapies.

Neuropsychological profile in the preclinical stages of dementia: principal component analysis approach

The preclinical stages of dementia include subtle neurocognitive changes that are not easily detected in standard clinical evaluations. Neuropsychological evaluation is important for the classification and prediction of deterioration in all the phases of dementia.

Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern

Alzheimer’s disease (AD) is a disease of a heterogeneous nature, which can be disentangled by exploring the characteristics of each AD subtype in the brain structure, neuropathology, and cognition. In this study, a total of 192 AD and 228 cognitively normal (CN) subjects were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Based on the cortical thickness patterns, the mixture of experts method (MOE) was applied to the implicit model spectrum of transforms lined with each AD subtype, then their neuropsychological and neuropathological characteristics were analyzed. Furthermore, the piecewise linear classifiers composed of each AD subtype and CN were resolved, and each subtype was comprehensively explained. The following four distinct AD subtypes were discovered: bilateral parietal, frontal, and temporal atrophy AD subtype (occipital sparing AD subtype (OSAD), 29.2%), left temporal dominant atrophy AD subtype (LTAD, 22.4%), minimal atrophy AD subtype (MAD, 16.1%), and diffuse atrophy AD subtype (DAD, 32.3%). These four subtypes display their own characteristics in atrophy pattern, cognition, and neuropathology. Compared with the previous studies, our study found that some AD subjects showed obvious asymmetrical atrophy in left lateral temporal-parietal cortex, OSAD presented the worst cerebrospinal fluid levels, and MAD had the highest proportions of APOE ε4 and APOE ε2. The subtype characteristics were further revealed from the aspect of the model, making it easier for clinicians to understand. The results offer an effective support for individual diagnosis and prognosis.

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.

Association of Gene Polymorphisms in APOE and BIN1 With Dementia of Alzheimer's Type Susceptibility in Chinese Han Population

Objectives: Dementia of the Alzheimer's type (DAT) is the most common chronic neurodegenerative disease. At present, the pathogenesis of DAT is not completely clear, and there are no drugs that can cure the disease. Once an individual is diagnosed with DAT, the survival time is only 3 to 9 years. Therefore, there is an urgent need to determine the etiology of DAT and the associated influencing factors to find a breakthrough in the treatment of DAT. Methods: We studied the relationship between polymorphisms in several genes (including BIN1 and APOE) and DAT susceptibility and the effects of sex differences on DAT. Our study included 137 patients with DAT and 509 healthy controls (HCs). Results: The APOE rs429358 polymorphism CC and CT genotypes were associated with an increased risk of DAT in women. We found a significant association between APOE ε4 and DAT. The frequency of the ε4 allele in the DAT group (15.5%) was higher than that in the HC group (8.7%). The BIN1 rs7561528 polymorphism was associated with a decreased risk of DAT in men. Conclusions: APOE gene rs429358 and BIN1 gene 7561528 genes may affect the susceptibility to DAT in a Chinese Han population.

Alterations in resting-state network dynamics along the Alzheimer's disease continuum

Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer's disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.

Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges

Recent data-sharing initiatives of clinical and preclinical Alzheimer's disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.

Genetic Analysis of Chinese Patients with Early-Onset Dementia Using Next-Generation Sequencing

Objective

Early-onset dementia (EOD) is a relatively uncommon form of dementia that afflicts people before age 65. Only a few studies analyzing the genetics of EOD have been performed in the Chinese Han population. Diagnosing EOD remains a challenge due to the diverse genetic and clinical heterogeneity of these diseases. The aim of this study was to investigate the genetic spectrum and clinical features of Chinese patients with EOD.

Materials and Methods

A total of 49 EOD patients were recruited. Targeted next-generation (NGS) analyses were performed to screen for all of the known genes associated with dementia. Possible pathogenic variants were confirmed by performing Sanger sequencing. The genetic spectrum and clinical features of the EOD patients were analyzed.

Results

Seven previously reported pathogenic variants (p.I213T and p.W165C in PSEN1; p.D678N in APP; c.1349_1352del in TBK1; p.P301L and p.R406W in MAPT; p.R110C in NOTCH3) and two novel variants of uncertain significance (p.P436L in PSEN2; c.239-11G>A in TARDBP) were identified.

Conclusion

Our study demonstrated the genetic spectrum and clinical features of EOD patients, and it reveals that genetic testing of known causal genes in EOD patients can help to make a precise diagnosis.

Posterior Cortical Atrophy: Characteristics From a Clinical Data Registry

Background: Posterior cortical atrophy (PCA) is a neurodegenerative syndrome that presents with higher-order visual dysfunction with relative sparing of memory and other cognitive domains, and it is most commonly associated with Alzheimer's disease pathology. There is a lack of data regarding the presentation of PCA to non-cognitive specialists. Therefore, we collected clinical data from neuro-ophthalmologists regarding the presentation of PCA to their practices and compared data to published cohorts and a published survey of cognitive specialists.

Methods: Members of the North American Neuro-Ophthalmology Society Listserv (NANOSnet) were invited to complete an online, retrospective, chart-review data-entry survey regarding their patients with PCA, and REDCap was used for data collection.

Results: Data for 38 patients were entered by 12 neuro-ophthalmologists. Patient mean age at presentation was 67.8 years, and 74% of patients were women. Difficulty reading was reported at presentation by 91% of patients, and poor performance on color vision, stereopsis, and visual field testing (performed reliably by 36/38 patients) were common findings. Most patients who were treated were treated with donepezil and/or memantine.

Conclusions: Compared to published data from cognitive specialists, patients presenting to neuro-ophthalmology with PCA were more likely to be older and female and have a reading complaint. Reliable visual field testing was the norm with homonymous defects in the majority of patients. The neuro-ophthalmologist plays an important role in diagnosing PCA in older adults with unexplained visual signs and symptoms, and future studies of PCA should involve multiple specialists in order to advance our understanding of PCA and develop effective treatments.

Decreased excitability of locus coeruleus neurons during hypercapnia is exaggerated in the streptozotocin-model of Alzheimer's disease

The locus coeruleus (LC) is a pontine nucleus important for respiratory control and central chemoreception. It is affected in Alzheimer's disease (AD) and alteration of LC cell function may account for respiratory problems observed in AD patients. In the current study, we tested the electrophysiological properties and CO₂/pH sensitivity of LC neurons in a model for AD. Sporadic AD was induced in rats by intracerebroventricular injection of 2 mg/kg streptozotocin (STZ), which induces behavioral and molecular impairments found in AD. LC neurons were recorded using the patch clamp technique and tested for responses to CO₂ (10% CO₂, pH = 7.0). The majority (~60%) of noradrenergic LC neurons in adult rats were inhibited by CO₂ exposure as indicated by a significant decrease in action potential (AP) discharge to step depolarizations. The STZ-AD rat model had a greater sensitivity to CO₂ than controls. The increased CO₂-sensitivity was demonstrated by a significantly stronger inhibition of activity during hypercapnia that was in part due to hyperpolarization of the resting membrane potential. Reduction of AP discharge in both groups was generally accompanied by lower LC network activity, depolarized AP threshold, increased AP repolarization, and increased current through a subpopulation of voltage-gated K⁺ channels (K_V). The latter was indicated by enhanced transient K_V currents particularly in the STZ-AD group. Interestingly, steady-state K_V currents were reduced under hypercapnia, a change that would favor enhanced AP discharge. However, the collective response of most LC neurons in adult rats, and particularly those in the STZ-AD group, was inhibited by CO₂.