Seasonal plasticity of cognition and related biological measures in adults with and without Alzheimer disease: Analysis of multiple cohorts

Whole-genome sequencing study in Koreans identifies novel loci for Alzheimer's disease

INTRODUCTION

The genetic basis of Alzheimer's disease (AD) in Koreans is poorly understood.

METHODS

We performed an AD genome-wide association study using whole-genome sequence data from 3540 Koreans (1583 AD cases, 1957 controls) and single-nucleotide polymorphism array data from 2978 Japanese (1336 AD cases, 1642 controls). Significant findings were evaluated by pathway enrichment and differential gene expression analysis in brain tissue from controls and AD cases with and without dementia prior to death.

RESULTS

We identified genome-wide significant associations with APOE in the total sample and ROCK2 (rs76484417, p = 2.71×10-8) among APOE ε4 non-carriers. A study-wide significant association was found with aggregated rare variants in MICALL1 (MICAL like 1) (p = 9.04×10-7). Several novel AD-associated genes, including ROCK2 and MICALL1, were differentially expressed in AD cases compared to controls (p < 3.33×10-3). ROCK2 was also differentially expressed between AD cases with and without dementia (p = 1.34×10-4).

DISCUSSION

Our results provide insight into genetic mechanisms leading to AD and cognitive resilience in East Asians.

HIGHLIGHTS

Novel genome-wide significant associations for AD identified with ROCK2 and MICALL1. ROCK2 and MICALL1 are differentially expressed between AD cases and controls in the brain. This is the largest whole-genome-sequence study of AD in an East Asian population.

Association of vitamin D and cognition in people with type 2 diabetes: a systematic review

CONTEXT

There is a high prevalence of vitamin D deficiency and impaired cognitive function in people with type 2 diabetes mellitus (T2DM).

OBJECTIVE

To critically and systematically review the literature on the association between vitamin D status and cognitive performance in people with type 2 diabetes.

DATA SOURCES

This review was conducted according to PRISMA recommendations. MEDLINE, SCOPUS, the Cochrane Library, and Web of Science databases were searched using the terms "Diabetes Mellitus, Type 2", "Cognitive Function", and "Vitamin D".

DATA EXTRACTION

Eight observational and 1 randomized study were included, containing data of 14 648 adult and elderly individuals (19-74 y). All extracted data were compiled, compared, and critically analyzed.

DATA ANALYSIS

There is no strong evidence that lower serum concentrations of vitamin D and vitamin D-binding protein are associated with worsening cognitive function in individuals with T2DM. Vitamin D supplementation (12 wk) improved the scores of some executive functioning tests, although there was no difference between low doses (5000 IU/wk) and high doses (50 000 IU/wk).

CONCLUSIONS

There is no high-quality evidence demonstrating an association between vitamin D status and cognitive function, or clinical benefits on cognition from vitamin D supplementation in individuals with T2DM. Future studies are needed. Systematic Review Registration: PROSPERO registration no. CRD42021261520.

Assessing the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust

About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.

Homo temporus: Seasonal Cycles as a Fundamental Source of Variation in Human Psychology

Many animal species exhibit seasonal changes in their physiology and behavior. Yet despite ample evidence that humans are also responsive to seasons, the impact of seasonal changes on human psychology is underappreciated relative to other sources of variation (e.g., personality, culture, development). This is unfortunate because seasonal variation has potentially profound conceptual, empirical, methodological, and practical implications. Here, we encourage a more systematic and comprehensive collective effort to document and understand the many ways in which seasons influence human psychology. We provide an illustrative summary of empirical evidence showing that seasons impact a wide range of affective, cognitive, and behavioral phenomena. We then articulate a conceptual framework that outlines a set of causal mechanisms through which seasons can influence human psychology-mechanisms that reflect seasonal changes not only in meteorological variables but also in ecological and sociocultural variables. This framework may be useful for integrating many different seasonal effects that have already been empirically documented and for generating new hypotheses about additional seasonal effects that have not yet received empirical attention. The article closes with a section that provides practical suggestions to facilitate greater appreciation for, and systematic study of, seasons as a fundamental source of variation in human psychology.

Integrated partially linear model for multi-center studies with heterogeneity and batch effect in covariates

The design of multi-center study is increasingly used for borrowing strength from multiple research groups to obtain broadly applicable and reproducible study findings. Regression analysis is widely used for analyzing multi-group studies, however, some of the large number of regression predictors are nonlinear and/or often measured with batch effects in many large scale collaborative studies. Also, the group compositions of the nonlinear predictors are potentially heterogeneous across different centers. The conventional pooled data analysis ignores the interplay between nonlinearity and batch effect, group composition heterogeneity, measurement error and other data incoherence in multi-center setting that can cause biased regression estimates and misleading outcomes. In this paper, we propose an integrated partially linear regression model (IPLM) based analysis to account for the predictor's nonlinearity, general batch effect, group composition heterogeneity, high-dimensional covariates, potential measurement-error in covariates, and combinations of these complexities simultaneously. A local linear regression based approach is employed to estimate the nonlinear component and a regularization procedure is introduced to identify the predictors' effects that can be either homogeneous or heterogeneous across groups. In particular, when the effects of all predictors are homogeneous across the study centers, the proposed IPLM can automatically reduce to one single parsimonious partially linear model for all centers. The proposed method has asymptotic estimation and variable selection consistency including high-dimensional covariates. Moreover, it has a fast computing algorithm and its effectiveness is supported by numerical simulation studies. A multi-center Alzheimer's disease research project is provided to illustrate the proposed IPLM based analysis.

Seasonal variations of functional connectivity of human brains

Seasonal variations have long been observed in various aspects of human life. While there is an abundance of research that has characterized seasonality effects in, for example, cognition, mood, and behavior, including studies of underlying biophysical mechanisms, direct measurements of seasonal variations of brain functional activities have not gained wide attention. We have quantified seasonal effects on functional connectivity as derived from MRI scans. A cohort of healthy human subjects was divided into four groups based on the seasons of their scanning dates as documented in the image database of the Human Connectome Project. Sinusoidal functions were used as regressors to determine whether there were significant seasonal variations in measures of brain activities. We began with the analysis of seasonal variations of the fractional amplitudes of low frequency fluctuations of regional functional signals, followed by the seasonal variations of functional connectivity in both global- and network-level. Furthermore, relevant environmental factors, including average temperature and daylength, were found to be significantly associated with brain functional activities, which may explain how the observed seasonal fluctuations arise. Finally, topological properties of the brain functional network also showed significant variations across seasons. All the observations accumulated revealed seasonality effects of human brain activities in a resting-state, which may have important practical implications for neuroimaging research.

Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies

Background

Alzheimer's disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance.

Methods

The integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging.

Results

The pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets.

Conclusion

The pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging.

Daytime Light Deficiency Leads to Sex- and Brain Region-Specific Neuroinflammatory Responses in a Diurnal Rodent

Seasonal changes in peripheral inflammation are well documented in both humans and animal models, but seasonal changes in neuroinflammation, especially the impact of seasonal lighting environment on neuroinflammation remain unclear. To address this question, the present study examined the effects of environmental lighting conditions on neuroinflammation in a diurnal rodent model, Nile grass rats (Arvicanthis niloticus). Male and female grass rats were housed in either bright (brLD) or dim (dimLD) light during the day to simulate a summer or winter light condition, respectively. After 4 weeks, microglia markers Iba-1 and CD11b, as well as pro-inflammatory cytokines TNF-α and IL-6, were examined in the anterior cingulate cortex (ACC), basolateral amygdala (BLA), and dorsal hippocampus (dHipp). The results revealed that winter-like dim light during the day leads to indicators of increased neuroinflammation in a brain site- and sex-specific manner. Specifically, relatively few changes in the neuroinflammatory markers were observed in the ACC, while numerous changes were found in the BLA and dHipp. In the BLA, winter-like dimLD resulted in hyper-ramified microglia morphology and increased expression of the pro-inflammatory cytokine IL-6, but only in males. In the dHipp, dimLD led to a higher number and hyper-ramified morphology of microglia as well as increased expression of CD11b and TNF-α, but only in females. Neuroinflammatory state is thus influenced by environmental light, differently in males and females, and could play a role in sex differences in the prevalence and symptoms of psychiatric or neurological disorders that are influenced by season or other environmental light conditions. Diurnal Nile grass rats were housed under bright or dim light during the day for 4 weeks, simulating seasonal fluctuations in daytime lighting environment. Dim light housing resulted in hyper-ramified morphology of microglia (scale bar, 15 μm) and altered expression of pro-inflammatory cytokines (TNF-α) in a sex- and brain region-specific manner.

Can circadian rhythm predict changes in neurocognitive functioning in bipolar disorder: protocol of a 12-month longitudinal cohort study based on research domain criteria

Introduction: Bipolar disorder (BD) is a prevalent and disabling mental disorder characterized by disrupted circadian rhythms and impaired neurocognitive features, both of which fall under the major domains of Research Domain Criteria (RDoC). However, there is limited evidence regarding the interaction between circadian rhythms and long-term neurocognitive functioning. Therefore, this longitudinal cohort study protocol aims to explore whether circadian rhythm can predict changes in neurocognitive functioning over time in patients with BD.Methods: This study adopts a longitudinal cohort design, aiming to recruit 100 BD patients in either depressive or remitted states. Participants will undergo evaluations from clinical, circadian rhythm, and neurocognitive perspectives at baseline, 6-month, and 12-month follow-ups, involving questionnaires, actigraphy, and computed neurocognitive tests. We will examine both cross-sectional and longitudinal associations between participants' circadian rhythm patterns and neurocognitive functioning. Statistical analyses will employ Spearman correlation and mixed regression models.Discussion: We anticipate that circadian rhythms may serve as predictors of neurocognitive functioning changes. The findings of this study could offer supplementary insights into BD pathophysiology, potential treatment targets, and prediction.Trial Registration: This study has been registered with the Chinese Clinical Trial Registry under the registration code ChiCTR2200064922 on 21st October 2022.

Interpretable deep learning of myelin histopathology in age-related cognitive impairment

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aβ) plays a major role in Alzheimer's type age-related cognitive impairment, in addition to other etiopathologies such as Aβ-independent hyperphosphorylated tau, cerebrovascular disease, and myelin damage, which also warrant further investigation. Classical methods, even in the setting of the gold standard of postmortem brain assessment, involve semi-quantitative ordinal staging systems that often correlate poorly with clinical outcomes, due to imperfect cognitive measurements and preconceived notions regarding the neuropathologic features that should be chosen for study. Improved approaches are needed to identify histopathological changes correlated with cognition in an unbiased way. We used a weakly supervised multiple instance learning algorithm on whole slide images of human brain autopsy tissue sections from a group of elderly donors to predict the presence or absence of cognitive impairment (n = 367 with cognitive impairment, n = 349 without). Attention analysis allowed us to pinpoint the underlying subregional architecture and cellular features that the models used for the prediction in both brain regions studied, the medial temporal lobe and frontal cortex. Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with a modest accuracy that was significantly greater than chance. Attention-based interpretation studies of the features most associated with cognitive impairment in the top performing models suggest that they identified myelin pallor in the white matter. Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive impairment that can be translated to the study of other neurobiological disorders.

Posterior cingulate cortex reveals an expression profile of resilience in cognitively intact elders

The posterior cingulate cortex, a key hub of the default mode network, underlies autobiographical memory retrieval and displays hypometabolic changes early in Alzheimer disease. To obtain an unbiased understanding of the molecular pathobiology of the aged posterior cingulate cortex, we performed RNA sequencing (RNA-seq) on tissue obtained from 26 participants of the Rush Religious Orders Study (11 males/15 females; aged 76-96 years) with a pre-mortem clinical diagnosis of no cognitive impairment and post-mortem neurofibrillary tangle Braak Stages I/II, III, and IV. Transcriptomic data were gathered using next-generation sequencing of RNA extracted from posterior cingulate cortex generating an average of 60 million paired reads per subject. Normalized expression of RNA-seq data was calculated using a global gene annotation and a microRNA profile. Differential expression (DESeq2, edgeR) using Braak staging as the comparison structure isolated genes for dimensional scaling, associative network building and functional clustering. Curated genes were correlated with the Mini-Mental State Examination and semantic, working and episodic memory, visuospatial ability, and a composite Global Cognitive Score. Regulatory mechanisms were determined by co-expression networks with microRNAs and an overlap of transcription factor binding sites. Analysis revealed 750 genes and 12 microRNAs significantly differentially expressed between Braak Stages I/II and III/IV and an associated six groups of transcription factor binding sites. Inputting significantly different gene/network data into a functional annotation clustering model revealed elevated presynaptic, postsynaptic and ATP-related expression in Braak Stages III and IV compared with Stages I/II, suggesting these pathways are integral for cognitive resilience seen in unimpaired elderly subjects. Principal component analysis and Kruskal-Wallis testing did not associate Braak stage with cognitive function. However, Spearman correlations between genes and cognitive test scores followed by network analysis revealed upregulation of classes of synaptic genes positively associated with performance on the visuospatial perceptual orientation domain. Upregulation of key synaptic genes suggests a role for these transcripts and associated synaptic pathways in cognitive resilience seen in elders despite Alzheimer disease pathology and dementia.

Feasibility of Digital Memory Assessments in an Unsupervised and Remote Study Setting

Sensitive and frequent digital remote memory assessments via mobile devices hold the promise to facilitate the detection of cognitive impairment and decline. However, in order to be successful at scale, cognitive tests need to be applicable in unsupervised settings and confounding factors need to be understood. This study explored the feasibility of completely unsupervised digital cognitive assessments using three novel memory tasks in a Citizen Science project across Germany. To that end, the study aimed to identify factors associated with stronger participant retention, to examine test-retest reliability and the extent of practice effects, as well as to investigate the influence of uncontrolled settings such as time of day, delay between sessions or screen size on memory performance. A total of 1,407 adults (aged 18-89) participated in the study for up to 12 weeks, completing weekly memory tasks in addition to short questionnaires regarding sleep duration, subjective cognitive complaints as well as cold symptoms. Participation across memory tasks was pseudorandomized such that individuals were assigned to one of three memory paradigms resulting in three otherwise identical sub-studies. One hundred thirty-eight participants contributed to two of the three paradigms. Critically, for each memory task 12 independent parallel test sets were used to minimize effects of repeated testing. First, we observed a mean participant retention time of 44 days, or 4 active test sessions, and 77.5% compliance to the study protocol in an unsupervised setting with no contact between participants and study personnel, payment or feedback. We identified subject-level factors that contributed to higher retention times. Second, we found minor practice effects associated with repeated cognitive testing, and reveal evidence for acceptable-to-good retest reliability of mobile testing. Third, we show that memory performance assessed through repeated digital assessments was strongly associated with age in all paradigms, and individuals with subjectively reported cognitive decline presented lower mnemonic discrimination accuracy compared to non-complaining participants. Finally, we identified design-related factors that need to be incorporated in future studies such as the time delay between test sessions. Our results demonstrate the feasibility of fully unsupervised digital remote memory assessments and identify critical factors to account for in future studies.

Estimations of the weather effects on brain functions using functional MRI: A cautionary note

The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross‐sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting‐state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting‐state parameters, including the amplitude of low‐frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross‐validation using the resting‐state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long‐term effects using MRI.

A Topical Emollient Mitigates the Progression of Cognitive Impairment in the Elderly: A Randomized, Open-Label Pilot Trial

BACKGROUND

Cognitive impairment is common in the elderly. Prior studies suggest a link between chronic inflammation and cognitive dysfunction, while aging-associated epidermal dysfunction has been connected to elevations in circulating cytokines.

OBJECTIVE

We assessed here whether improvements in epidermal function can mitigate the progression of cognitive impairment.

METHODS

This randomized, open-label pilot trial was carried out in two cities in northern China. A total of 200 participants aged ≥65 years were randomly assigned to the emollient-treated and untreated groups at 1:1 ratio. Participants in the treated group were treated topically with Atopalm cream® twice-daily from November to the following May each year for three consecutive years, while the untreated subjects served as controls. The Global Deterioration Scale (GDS) was used to assess the severity of cognitive impairment, while epidermal biophysical properties were measured on the forearms and the shins in parallel.

RESULTS

Over the three-year trial, GDS significantly increased from baseline (p<0.0001) in the controls, while in the treated group, GDS stabilized. While stratum corneum hydration on the forearms did not change significantly in the controls, transepidermal water loss rates (TEWL), significantly increased by the end of the trial compared to baselines in the controls (p<0.0001). On the forearms of the treated group, stratum corneum hydration increased (p<0.0001) while skin surface pH decreased from baseline (p<0.0001).

CONCLUSIONS

These results suggest that improvements in epidermal function with topical emollient can mitigate the progression of cognitive impairment. However, the sample size was relatively small, and trials in a larger cohort are needed to validate the present results.

Effect of seasons on delirium in postoperative critically ill patients: a retrospective analysis

BACKGROUND AND OBJECTIVES

Postoperative delirium is common in critically ill patients and is known to have several predisposing and precipitating factors. Seasonality affects cognitive function which has a more dysfunctional pattern during winter. We, therefore, aimed to test whether seasonal variation is associated with the occurrence of delirium and hospital Length Of Stay (LOS) in critically ill non-cardiac surgical populations.

METHODS

We conducted a retrospective analysis of adult patients recovering from non-cardiac surgery at the Cleveland Clinic between March 2013 and March 2018 who stayed in Surgical Intensive Care Unit (SICU) for at least 48 hours and had daily Confusion Assessment Method Intensive Care Unit (CAM-ICU) assessments for delirium. The incidence of delirium and LOS were summarized by season and compared using chi-square test and non-parametric tests, respectively. A logistic regression model was used to assess the association between delirium and LOS with seasons, adjusted for potential confounding variables.

RESULTS

Among 2300 patients admitted to SICU after non-cardiac surgeries, 1267 (55%) had postoperative delirium. The incidence of delirium was 55% in spring, 54% in summer, 55% in fall and 57% in winter, which was not significantly different over the four seasons (p = 0.69). The median LOS was 12 days (IQR = [8, 19]) overall. There was a significant difference in LOS across the four seasons (p = 0.018). LOS during summer was 12% longer (95% CI: 1.04, 1.21; p = 0.002) than in winter.

CONCLUSIONS

In adult non-cardiac critically ill surgical patients, the incidence of postoperative delirium is not associated with season. Noticeably, LOS was longer in summer than in winter.

Query-based-learning mortality-related decoders for the developed island economy

Search volumes from Google Trends over clear-defined temporal and spatial scales were reported beneficial in predicting influenza or disease outbreak. Recent studies showed Wiener Model shares merits of interpretability, implementation, and adaptation to nonlinear fluctuation in terms of real-time decoding. Previous work reported Google Trends effectively predicts death-related trends for the continent economy, yet whether it applies to the island economy is unclear. To this end, a framework of the mortality-related model for a developed island economy Taiwan was built based on potential death causes from Google Trends, aiming to provide new insights into death-related online search behavior at a population level. Our results showed estimated trends based on the Wiener model significantly correlated to actual trends, outperformed those with multiple linear regression and seasonal autoregressive integrated moving average. Meanwhile, apart from that involved all possible features, two other sets of feature selecting strategies were proposed to optimize pre-trained models, either by weights or waveform periodicity of features, resulting in estimated death-related dynamics along with spectrums of risk factors. In general, high-weight features were beneficial to both "die" and "death", whereas features that possessed clear periodic patterns contributed more to "death". Of note, normalization before modeling improved decoding performances.

Progress on the roles of MEF2C in neuropsychiatric diseases

Myocyte Enhancer Factor 2 C (MEF2C), one of the transcription factors of the MADS-BOX family, is involved in embryonic brain development, neuronal formation and differentiation, as well as in the growth and pruning of axons and dendrites. MEF2C is also involved in the development of various neuropsychiatric disorders, such as autism spectrum disorders (ASD), epilepsy, schizophrenia and Alzheimer’s disease (AD). Here, we review the relationship between MEF2C and neuropsychiatric disorders, and provide further insights into the mechanism of these diseases.

Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD): A Report of an International Research Collaboration Network

Longitudinal observational cohort studies are being conducted worldwide to understand cognition, biomarkers, and the health of the aging population better. Cross-cohort comparisons and networks of registries in Alzheimer's disease (AD) foster scientific exchange, generate insights, and contribute to the evolving clinical science in AD. A scientific working group was convened with invited investigators from established cohort studies in AD, in order to form a research collaboration network as a resource to address important research questions. The Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD) collaboration network was created to bring together global resources and expertise, to generate insights and improve understanding of the natural history of AD, to inform design of clinical trials in all disease stages, and to plan for optimal patient access to disease-modifying therapies once they become available. The network brings together expertise and data insights from 7 cohorts across Australia, Europe, and North America. Notably, the network includes populations recruited through memory clinics as well as population-based cohorts, representing observations from individuals across the AD spectrum. This report aims to introduce the CONCORD-AD network, providing an overview of the cohorts involved, reporting the common assessments used, and describing the key characteristics of the cohort populations. Cohort study designs and baseline population characteristics are compared, and available cognitive, functional, and neuropsychiatric symptom data, as well as the frequency of biomarker assessments, are summarized. Finally, the challenges and opportunities of cross-cohort studies in AD are discussed.

Global Public Interest and Seasonal Variations in Alzheimer's Disease: Evidence From Google Trends

Background: As the world's population ages, Alzheimer's disease (AD), a common neurodegenerative disease, is a major challenge to human health in the future. Understanding the information needs on AD of the global public can contribute to the prevention and control of AD. The purpose of this study was to explore global public interest and seasonal variations in AD using Google Trends (GT). Methods: GT was used to obtain relative search volume (RSV) of the keyword "Alzheimer's disease" in six English-speaking countries (Australia, New Zealand, the USA, the UK, Canada, and Ireland) and the world from January 2004 to December 2020. Cosinor analysis was applied to detect the seasonality of AD-related RSV. Time series plot was used to observe the trend of annual mean AD-related RSV. Globally, hot topics and top rising topics related to AD were also analyzed. In addition, we also explored the geographical distribution characteristics of AD-related RSV. Results: AD-related RSV declined steadily from January 2004 to December 2013 and rose steadily from January 2014 to December 2020. Search popularity of AD is low in the southern hemisphere, compared to the northern hemisphere. Cosinor analysis showed that there were significant seasonal variations in AD-related RSV in six English-speaking countries (all P < 0.05). Interestingly, regardless of the hemisphere, peaks were observed in the winter months and trough in the summer months. Topics related to the characteristics and etiology of AD, early onset AD, AD-related associations, care of AD patients, and diseases that can easily be confused with AD had received special attention. Conclusions: There is increasing global public interest for AD and a significant seasonal variation in AD. A better understanding of the seasonal variations and public interest of AD by governments, health workers and patients can contribute to the prevention, management, and treatment of AD.

Seasonality of cognitive function in the general population: the Rotterdam Study

Seasonal variation in cognitive function and underlying cerebral hemodynamics in humans has been suggested, but not consistently shown in previous studies. We assessed cognitive function in 10,276 participants from the population-based Rotterdam Study, aged 45 years and older without dementia, at baseline and at subsequent visits between 1999 and 2016. Seasonality of five cognitive test scores and of a summary measure of global cognition were determined, as well as of brain perfusion. Using linkage with medical records, we also examined whether a seasonal variation was present in clinical diagnoses of dementia. We found a seasonal variation of global cognition (0.05 standard deviations [95% confidence interval: 0.02–0.08]), the Stroop reading task, the Purdue Pegboard test, and of the delayed world learning test, with the best performance in summer months. In line with these findings, there were fewer dementia diagnoses of dementia in spring and summer than in winter and fall. We found no seasonal variation in brain perfusion. These findings support seasonality of cognition, albeit not explained by brain perfusion.

Imaging the temporal dynamics of brain states with highly sampled fMRI

The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.

The circadian rest-activity pattern predicts cognitive decline among mild-moderate Alzheimer's disease patients

BACKGROUND

Alterations in circadian rhythms are present in the presymptomatic stage of Alzheimer's disease (AD), possibly contributing to its pathogenesis. However, it is unknown whether such alterations are associated with worse outcomes once individuals are diagnosed with symptomatic disease. We aimed to evaluate the association between the circadian rest-activity pattern and AD-related features in patients with mild-moderate AD.

METHODS

We assessed the circadian rest-activity pattern of consecutive patients with mild-moderate AD through actigraphy for 14 days. Cerebrospinal fluid was obtained to determine the levels of important pathological markers including amyloid-beta protein (Aβ42), phosphorylated tau (P-tau), total tau (T-tau), and neurofilament light (NF-L). Neuropsychological evaluation was conducted at the beginning of the study and after 12 months of follow-up. Linear regression models were performed considering the global population and Aβ42+ patients only.

RESULTS

The cohort included 100 patients with mild-moderate AD. The median age [p25;p75] was 76.0 [73.0;80.0] years and 63.0% were female. Older age (effect size [SE] of 0.324 [0.096]; p = 0.001) and male sex (0.780 [0.193]; p = 0.001) were associated with increased fragmentation and decreased synchronization of the rhythm, respectively. After adjusting for age, sex, and season of the year, increased levels of T-tau (effect size [95% CI] of 0.343 [0.139 to 0.547]; p = 0.001) and NF-L (0.444 [0.212 to 0.676]; p = 0.001) were associated with a higher amplitude of the rest-activity rhythm. Increased fragmentation of the rhythm at baseline was associated with greater cognitive decline after one year of follow-up independent of age, sex, T-tau/Aβ42 ratio, educational level, and season of the year (- 0.715 [- 1.272 to - 0.157]; p = 0.013). Similar findings were obtained considering only the Aβ42+ patients.

CONCLUSIONS

Our results suggest a potential role of the circadian rest-activity pattern in predicting the cognitive decline of patients with mild-moderate AD. Further studies are warranted to confirm these findings and to elucidate whether there is causality among the observed associations.

Self-reported experiences of discrimination in older black adults are associated with insula functional connectivity

Self-reported experiences of discrimination are associated with a number of negative health outcomes. However, the neurobiological correlates of discrimination remain elusive. Recent neuroimaging work suggests that the amygdala is sensitive to forms of social adversity and the insula is involved in assessments of trust. We hypothesized that functional connectivity (FC) of these brain regions may be associated with discrimination in older Black adults. One-hundred and twenty-four nondemented older Black adults participating in the Minority Aging Research Study or the Clinical Core study of the Rush Alzheimer's Disease Center completed a measure of self-reported experiences of discrimination and a 3T MRI brain scan including structural T1 and resting-state fMRI EPIBOLD sequences. The right and left amygdala and insula regions were anatomically delineated as ROIs according to the Harvard-Oxford Brain Atlas and whole-brain voxelwise FC analyses were conducted using default parameters in the CONN toolbox. In regression analyses controlling for demographics and global cognition, self-reported experiences of discrimination were associated with greater FC between the left insula and the bilateral intracalcarine cortex, weaker FC between the left insula and the left dorsolateral prefrontal cortex, and weaker FC between the right insula and the left supplementary motor area. Amygdala analyses yielded no significant findings. Greater self-reported experiences of discrimination are associated with differential insula functional connectivity in older adults. More specifically, results suggest that discrimination is associated with differential connectivity of a key region (the insula) involved in trust perception.

Association between temperature exposure and cognition: a cross-sectional analysis of 20,687 aging adults in the United States

BACKGROUND

Older adults are particularly vulnerable to the adverse health effects of extreme temperature-related events. A growing body of literature highlights the importance of the natural environment, including air pollution and sunlight, on cognitive health. However, the relationship between exposure to outdoor temperatures and cognitive functioning, and whether there exists any differences across climate region, remains largely unexplored. We address this gap by examining the temperature-cognition association, and whether there exists any variation across climate regions in a national cohort of aging adults.

METHODS

In this cross-sectional study, we obtained data on temperature exposure based on geocoded residential location of participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. For each participant, this information was linked to their cognitive scores from Word List Learning and Recall tests to assess cognitive functioning. We used distributed lag non-linear models (dlnm) to model temperature effects over 2 days. Multivariable linear regression was used to compute temperature-cognitive functioning associations, adjusted for important covariates. Region-specific ("Dry", "Mediterranean/oceanic", "Tropical" and "Continental") associations were examined by including an interaction term between climate region and temperature.

RESULTS

Amongst 20,687 individuals (mean age = 67.8; standard deviation = 9.2), exposure to region-specific extreme cold temperatures in the "dry" region (e.g., Arizona) over 2 days was associated with lower cognitive scores (Mean Difference [MD]: -0.76, 95% Confidence Interval [CI]: - 1.45, - 0.07). Associations remained significant for cumulative effects of temperature over 2 days. Extremely cold exposure in the "Mediterranean/oceanic" region (e.g., California) over 2 days was also associated with significantly lower cognitive performance (MD: -0.25, 95% CI: - 0.47, - 0.04). No significant associations were observed for exposure to hot temperatures. Cognitive performance was slightly higher in late summer and fall compared to early summer.

CONCLUSION

We noted adverse cognitive associations with cold temperatures in traditionally warmer regions of the country and improved cognition in summer and early fall seasons. While we did not observe very large significant associations, this study deepens understanding of the impact of climate change on the cognitive health of aging adults and can inform clinical care and public health preparedness plans.

The Burden of Alzheimer's Disease Mortality in the United States, 1999-2018

BACKGROUND

The increasing prevalence of Alzheimer's disease (AD), along with the associated burden on healthcare systems, presents a substantial public health challenge.

OBJECTIVE

This study aimed to investigate trends in AD mortality and the relevant burden across the United States (U.S.) from 1999 to 2018 and to predict mortality trends between 2019 and 2023.

METHODS

Data on AD-related deaths between 1999 and 2018 were collected from the WONDER database administered by the U.S. Centers for Disease Control and Prevention (CDC). The Joinpoint Regression Program was used to analyze mortality trends due to AD. Years of life lost (YLL) were calculated to explore the burden of AD deaths. An autoregressive integrated moving average (ARIMA) model was employed to forecast mortality trends from 2019 to 2023.

RESULTS

Over a recent 20-year period, the number of AD deaths in the U.S. increased from 44,536 (31,145 females and 13,391 males) to 122,019 (84,062 females and 37,957 males). The overall age-adjusted mortality rate increased from 16.5/100,000 in 1999 to 30.5/100,000 in 2018. AD mortality is projected to reach 42.40/100000 within the year 2023. Overall, AD resulted in 322,773.00 YLL (2.33 per 1000 population) in 1999 and 658,501.87 YLL (3.68 per 1000 population) in 2018.

CONCLUSION

Our findings demonstrate an increase in AD mortality in the U.S. from 1999 to 2018 as well as a rapid increase from 2019 to 2023. The high burden of AD deaths emphasizes the need for targeted prevention, early diagnosis, and hierarchical management.

Orexin (hypocretin) mediates light-dependent fluctuation of hippocampal function in a diurnal rodent

Environmental lighting conditions play a central role in cognitive function, but the underlying mechanisms remain unclear. Utilizing a diurnal rodent model, the Nile grass rat (Arvicanthis niloticus), we previously found that daytime light intensity affects hippocampal function in this species in a manner similar to its effects in humans. Compared to animals housed in a 12:12 h bright light-dark (brLD) cycle, grass rats kept in a 12:12 h dim light-dark (dimLD) cycle showed impaired spatial memory in the Morris water maze (MWM) and reduced CA1 apical dendritic spine density. The present study explored the neural substrates mediating the effects of daylight intensity on hippocampal function focusing on the hypothalamic orexin (hypocretin) system. First, animals housed in dimLD were treated with daily intranasal administration of orexin A peptide over five training days of the MWM task. Compared to vehicle controls, this treatment led to superior spatial memory accompanied by increased phosphorylation of Ca²⁺ /calmodulin-dependent protein kinase II α and glutamate receptor 1 within the CA1. To assess the role of hippocampal orexinergic signaling, an adeno-associated viral vector (AAV) expressing an orexin receptor 1 (OX1R) shRNA was injected into the dorsal hippocampus targeting the CA1 of animals housed in brLD. AAV-mediated knockdown of OX1R within the hippocampus resulted in deficits in MWM performance and reduced CA1 apical dendritic spine density. These results are consistent with the view that the hypothalamic orexinergic system underlies the modulatory role of daytime illumination on hippocampal function in diurnal mammals.

Cycles in epilepsy

Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.

Effects of weather and season on human brain volume

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen’s d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

Diurnal and seasonal molecular rhythms in the human brain and their relation to Alzheimer disease

Diurnal and seasonal rhythms influence many aspects of human physiology including brain function. Moreover, altered diurnal and seasonal behavioral and physiological rhythms have been linked to Alzheimer's disease and related dementias (ADRD). Understanding the molecular basis for these links may lead to identification of novel targets to mitigate the negative impact of normal and abnormal diurnal and seasonal rhythms on ADRD or to alleviate the adverse consequences of ADRD on normal diurnal and seasonal rhythms. Diurnally and seasonally rhythmic gene expression and epigenetic modification in the human neocortex may be a key mechanism underlying these links. This chapter will first review the observed epidemiological links between normal and abnormal diurnal and seasonal rhythmicity, cognitive impairment, and ADRD. Then it will review normal diurnal and seasonal rhythms of brain epigenetic modification and gene expression in model organisms. Finally, it will review evidence for diurnal and seasonal rhythms of epigenetic modification and gene expression the human brain in aging, Alzheimer's disease, and other brain disorders.

Increasing the reproducibility of fluid biomarker studies in neurodegenerative studies

Biomarkers have revolutionized scientific research on neurodegenerative diseases, in particular Alzheimer's disease, transformed drug trial design, and are also increasingly improving patient management in clinical practice. A few key cerebrospinal fluid biomarkers have been robustly associated with neurodegenerative diseases. Several novel biomarkers are very promising, especially blood-based markers. However, many biomarker findings have had low reproducibility despite initial promising results. In this perspective, we identify possible sources for low reproducibility of studies on fluid biomarkers for neurodegenerative diseases, with a focus on Alzheimer's disease. We suggest guidelines for researchers and journal editors, with the aim to improve reproducibility of findings.

Weather Woes? Exploring Potential Links between Precipitation and Age-Related Cognitive Decline

Rain, snow, or ice may discourage older adults from leaving their homes with potential consequences for social isolation, decreased physical activity, and cognitive decline. This study is the first to examine potential links between annual precipitation exposure and cognitive function in a large population-based cohort of older Americans. We examined the association between precipitation (percent of days with snow or rain in the past year) and cognitive function in 25,320 individuals aged 45+ from the Reasons for Geographic and Racial Differences in Stroke Study. Linear mixed models assessed the relationship between precipitation and cognitive function, as well as rates of change in cognitive function with age. We found a non-linear relationship between precipitation and cognitive function. Compared to those exposed to infrequent precipitation (less than 20% of days with rain/snow in the past year), cognitive function was higher among older adults experiencing moderately frequent precipitation (20–40% of annual days with precipitation). However, beyond more than about 45% of days with precipitation in the past year, there was a negative association between precipitation and cognitive function, with faster rates of cognitive decline with age. These exploratory findings motivate further research to better understand the complex role of precipitation for late-life cognitive function.

25-OH Vitamin D Levels and Cognitive Performance: Longitudinal Assessment in a Healthy Aging Cohort

Background: Declining serum levels of 25-hydroxyvitamin D [25(OH)D, a biomarker of vitamin D status] with aging is a well-recognized phenomenon. However, scarce information is available on the relation between 25(OH)D levels and cognitive performance over time in older individuals. Our purpose was to evaluate, longitudinally, the association of 25(OH)D with cognitive function in a healthy older adults’ cohort.

Methods: Sixty-four individuals over 55 years-old with no cognitive impairment, clustered as healthy “Poor” and “Good” cognitive performers, were followed for an average of 18 months. Seasonal-adjusted 25(OH)D serum levels (measured by high-performance liquid chromatography-tandem mass spectrometry) were related, longitudinally, with cognitive (memory and general/executive) composite scores.

Results: Overall seasonal-adjusted median serum 25(OH)D level was of 47 nmol/l [interquartile range (IQR), 38–60 nmol/l]. A negative correlation between baseline 25(OH)D and the general/executive composite score was found in the “Poor” cognitive performers (r^s = −0.52, p = 0.006), an association lost after adjusting 25(OH)D levels for the season. No effect was found in both groups between seasonal-adjusted 25(OH)D levels and the variation of both memory and general/executive composites during follow-up when adjusted for age, gender and education level.

Conclusion: In this healthy older population with no cognitive impairment, lower serum levels of 25(OH)D were not longitudinally associated with poorer cognitive scores.

Strengths and Weaknesses of the Gray Mouse Lemur (Microcebus murinus) as a Model for the Behavioral and Psychological Symptoms and Neuropsychiatric Symptoms of Dementia

To face the load of the prevalence of Alzheimer’s disease in the aging population, there is an urgent need to develop more translatable animal models with similarities to humans in both the symptomatology and physiopathology of dementia. Due to their close evolutionary similarity to humans, non-human primates (NHPs) are of primary interest. Of the NHPs, to date, the gray mouse lemur (Microcebus murinus) has shown promising evidence of its translatability to humans. The present review reports the known advantages and limitations of using this species at all levels of investigation in the context of neuropsychiatric conditions. In this easily bred Malagasy primate with a relatively short life span (approximately 12 years), age-related cognitive decline, amyloid angiopathy, and risk factors (i.e., glucoregulatory imbalance) are congruent with those observed in humans. More specifically, analogous behavioral and psychological symptoms and neuropsychiatric symptoms of dementia (BPSD/NPS) to those in humans can be found in the aging mouse lemur. Aged mouse lemurs show typical age-related alterations of locomotor activity daily rhythms such as decreased rhythm amplitude, increased fragmentation, and increased activity during the resting-sleeping phase of the day and desynchronization with the light-dark cycle. In addition, sleep deprivation successfully induces cognitive deficits in adult mouse lemurs, and the effectiveness of approved cognitive enhancers such as acetylcholinesterase inhibitors or N-methyl-D-aspartate antagonists is demonstrated in sleep–deprived animals. This result supports the translational potential of this animal model, especially for unraveling the mechanisms underlying dementia and for developing novel therapeutics to prevent age-associated cognitive decline. In conclusion, actual knowledge of BPSD/NPS-like symptoms of age-related cognitive deficits in the gray mouse lemur and the recent demonstration of the similarity of these symptoms with those seen in humans offer promising new ways of investigating both the prevention and treatment of pathological aging.

No seasonal influence on cognitive performance in a national sample of older adults in New Zealand

OBJECTIVES

A recent North American study reported seasonal differences in cognitive functioning in older adults. We assessed seasonality of cognitive functioning in a large data set of older adults in New Zealand.

METHODS

The International Residential Assessment Instrument-Home Care (interRAI-HC) data set was analysed using a non-parametric method for testing seasonal distribution of cognitive and depression scale scores.

RESULTS

Participants were 73 285 New Zealanders 65 years and older who completed their first interRAI-HC assessment (mean age, 81.4 years; 57% female). We analysed this sample cross-tabulating season (summer, autumn, winter and spring) and the Cognitive Performance Scale (CPS) score (Kruskal-Wallis test, P = 0.45). Month-by-month CPS scores also demonstrated no variation (Spearman's test, P = 0.96). There was no association between season of assessment and the Depression Rating Scale score, ruling out variability in affect impacting on cognitive performance (Kruskal-Wallis test, P = 0.99).

CONCLUSION

Our findings, limited to the Southern Hemisphere, demonstrate a lack of seasonality in cognitive performance and impairment in older adults.

Network Analyses of Integrated Differentially Expressed Genes in Papillary Thyroid Carcinoma to Identify Characteristic Genes

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC.