Characterizing Neighborhood Vulnerabilities in Mild Cognitive Impairment using the Environmental Justice Index.

Background: There is a need for integration and comprehensive characterization of environmental determinants of Alzheimer's disease. The Environmental Justice Index (EJI) is a new measure that consolidates multiple environmental health hazards. Objective: This analysis aims to explore how environmental vulnerabilities vary by race/ethnicity and whether they predict cognitive outcomes in a clinical trial of mild cognitive impairment (MCI). Methods: We used data from a clinical trial of 107 MCI participants (28% minorities). Using the EJI, we extracted 40 measures of neighborhood environmental and social vulnerability including air and water pollution, access to recreational spaces, exposure to coal and lead mines, and area poverty. We also examined the relationship of the EJI to the Area Deprivation Index (ADI). Data was analyzed using regressions, correlations, and t-tests. Results: Environmental Burden Rank (EBR) across the sample (0.53±0.32) was near the 50th percentile nationally. When divided by race/ethnicity, environmental (p = 0.025) and social (p < 0.0001) vulnerabilities were significantly elevated for minorities, specifically for exposure to ozone, diesel particulate matter, carcinogenic air toxins, and proximity to treatment storage and disposal sites. ADI state decile was not correlated with the EBR. Neither EBR nor ADI were a significant predictor of cognitive decline. Conclusions: To our knowledge, this is the first study to link the EJI to an MCI trial. Despite limitations of a relatively small sample size, the study illustrates the potential of the EJI to provide deeper phenotyping of the exposome and diversity in clinical trial subjects.

The influence of economic policies on social environments and mental health.

Despite increased advocacy and investments in mental health systems globally, there has been limited progress in reducing mental disorder prevalence. In this paper, we argue that meaningful advancements in population mental health necessitate addressing the fundamental sources of shared distress. Using a systems perspective, economic structures and policies are identified as the potential cause of causes of mental ill-health. Neoliberal ideologies, prioritizing economic optimization and continuous growth, contribute to the promotion of individualism, job insecurity, increasing demands on workers, parental stress, social disconnection and a broad range of manifestations well-recognized to erode mental health. We emphasize the need for mental health researchers and advocates to increasingly engage with the economic policy discourse to draw attention to mental health and well-being implications. We call for a shift towards a well-being economy to better align commercial interests with collective well-being and social prosperity. The involvement of individuals with lived mental ill-health experiences, practitioners and researchers is needed to mobilize communities for change and influence economic policies to safeguard well-being. Additionally, we call for the establishment of national mental wealth observatories to inform coordinated health, social and economic policies and realize the transition to a more sustainable well-being economy that offers promise for progress on population mental health outcomes.

Malgré une meilleure sensibilisation et des investissements accrus dans les systèmes de santé mentale à travers le monde, les progrès en matière de réduction du degré de prévalence des troubles mentaux demeurent très limités. Dans le présent document, nous estimons que, pour réaliser des avancées au niveau de la santé mentale des populations, il est impératif de s'attaquer aux sources de cette détresse collective. En adoptant une perspective systémique, force est de constater que les politiques et structures économiques constituent les causes potentielles d'une mauvaise santé mentale. Les idéologies néolibérales, qui privilégient l'optimisation économique et la croissance ininterrompue, contribuent à promouvoir l'individualisme, l'insécurité professionnelle, la pression pesant sur les travailleurs, le stress parental, l'isolement social et un large éventail de facteurs associés à une dégradation de la santé mentale. Nous insistons sur la nécessité de faire appel à des chercheurs et défenseurs actifs dans ce domaine, afin de jouer un rôle dans la politique économique en attirant l'attention sur les implications pour le bien-être et la santé mentale. Nous plaidons pour une transition vers une économie du bien-être visant à rapprocher les intérêts commerciaux de la prospérité sociale et collective. L'intervention de personnes ayant été confrontées à des troubles mentaux, de praticiens et de chercheurs est nécessaire pour mobiliser les communautés en faveur d'un changement et influencer les politiques économiques pour préserver le bien-être. Par ailleurs, nous militons pour la création d'observatoires nationaux de la santé mentale qui serviront à orienter des politiques économiques, sociales et sanitaires coordonnées, mais aussi à favoriser l'évolution vers une économie du bien-être plus durable, laissant entrevoir une amélioration de la santé mentale au sein de la population.

A pesar del aumento de la promoción y las inversiones en sistemas de salud mental en todo el mundo, los avances en la reducción de la prevalencia de los trastornos mentales han sido limitados. En este documento, sostenemos que para lograr avances significativos en la salud mental de la población es necesario abordar las fuentes fundamentales de la angustia compartida. Mediante una perspectiva sistémica, las estructuras y políticas económicas se identifican como la posible causa de los problemas de salud mental. Las ideologías neoliberales, que priorizan la optimización económica y el crecimiento continuo, contribuyen al fomento del individualismo, la inseguridad laboral, el aumento de las exigencias a los trabajadores, el estrés parental, la desconexión social y una gran variedad de manifestaciones bien reconocidas que perjudican la salud mental. Insistimos en la necesidad de que los investigadores y los defensores de la salud mental se impliquen cada vez más en el discurso de la política económica para atraer la atención sobre las implicaciones para la salud mental y el bienestar. Pedimos un cambio hacia una economía del bienestar para alinear mejor los intereses comerciales con el bienestar colectivo y la prosperidad social. Para movilizar a las comunidades en favor del cambio e influir en las políticas económicas con el fin de salvaguardar el bienestar, es necesaria la participación de personas que han padecido enfermedades mentales, profesionales e investigadores. Además, pedimos la creación de observatorios nacionales de bienestar mental que sirvan de base a las políticas sanitarias, sociales y económicas coordinadas y permitan la transición a una economía del bienestar más sostenible, que ofrezca perspectivas de progreso en los resultados de salud mental de la población.

ElliQ, an AI-Driven Social Robot to Alleviate Loneliness: Progress and Lessons Learned.

Background: Loneliness is a significant issue in older adults and can increase the risk of morbidity and mortality. Objective: To present the development of ElliQ, a proactive, AI-driven social robot with multiple social and health coaching functions specifically designed to address loneliness and support older people. Development/Implementation: ElliQ, a consumer robot with a friendly appearance, uses voice, sounds, light, and buttons through a touch screen to facilitate conversation, music, video calls, well-being assessments, stress reduction, cognitive games, and health reminders. The robot was deployed by 15 government agencies in the USA. Initial experience suggests it is not only highly engaging for older people but may be able to improve their quality of life and reduce loneliness. In addition, the development of a weekly report that patients can share with their clinicians to allow better integration into routine care is described. Conclusion: This paper describes the development and real-world implementation of this product innovation and discusses challenges encountered and future directions.

Sex-specific blood biomarkers linked to memory changes in middle-aged adults: The Framingham Heart Study.

The relationship between sex-specific blood biomarkers and memory changes in middle-aged adults remains unclear. We aimed to investigate this relationship using the data from the Framingham Heart Study (FHS). We conducted association analysis, partial correlation analysis, and causal dose-response curves using blood biomarkers and other data from 793 middle-aged participants (≤ 60 years) from the FHS Offspring Cohort. The results revealed associations of adiponectin and fasting blood glucose with midlife memory change, along with a U-shaped relationship of high-density lipoprotein cholesterol with memory change. No significant associations were found for the other blood biomarkers (e.g., amyloid beta protein 42) with memory change. To our knowledge, this is the first sex-specific network analysis of blood biomarkers related to midlife memory change in a prospective cohort study. Our findings highlight the importance of targeting cardiometabolic risks and the need to validate midlife-specific biomarkers that can accelerate the development of primary preventive strategies.

Deep learning-aided decision support for diagnosis of skin disease across skin tones.

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease.

Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.

Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade.

BACKGROUND: Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects. OBJECTIVES: This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data. DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS: We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data. RESULTS: Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories. CONCLUSION: Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.

Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts.

The Patient Health Questionnaire 9 (PHQ-9) is the current standard outpatient screening tool for measuring and tracking the nine symptoms of major depressive disorder (MDD). While the PHQ-9 was originally conceptualized as a unidimensional measure, it has become clear that MDD is not a monolithic construct, as evidenced by high comorbidities with other theoretically distinct diagnoses and common symptom overlap between depression and other diagnoses. Therefore, identifying reliable and temporally stable subfactors of depressive symptoms could allow research and care to be tailored to different depression phenotypes. This study improved on previous factor analysis studies of the PHQ-9 by leveraging samples that were clinical (participants with depression only), large (N = 1483 depressed individuals in total), longitudinal (up to 5 years), and from three diverse (matching racial distribution of the United States) datasets. By refraining from assuming the number of factors or item loadings a priori, and thus utilizing a solely data-driven approach, we identified a ranked list of best-fitting models, with the parsimonious one achieving good model fit across studies at most timepoints (average TLI >= 0.90). This model categorizes the PHQ-9 items into four factors: (1) Affective (Anhedonia + Depressed Mood), (2) Somatic (Sleep + Fatigue + Appetite), (3) Internalizing (Worth/Guilt + Suicidality), (4) Sensorimotor (Concentration + Psychomotor), which may be used to further precision psychiatry by testing factor-specific interventions in research and clinical settings.

Expectancy Does Not Predict 18-month Treatment Outcomes with Cognitive Training in Mild Cognitive Impairment.

BACKGROUND: Computerized cognitive training (CCT) has emerged as a potential treatment option for mild cognitive impairment (MCI). It remains unclear whether CCT's effect is driven in part by expectancy of improvement. OBJECTIVES: This study aimed to determine factors associated with therapeutic expectancy and the influence of therapeutic expectancy on treatment effects in a randomized clinical trial of CCT versus crossword puzzle training (CPT) for older adults with MCI. DESIGN: Randomized clinical trial of CCT vs CPT with 78-week follow-up. SETTING: Two-site study - New York State Psychiatric Institute and Duke University Medical Center. PARTICIPANTS: 107 patients with MCI. INTERVENTION: 12 weeks of intensive training with CCT or CPT with follow-up booster training over 78 weeks. MEASUREMENTS: Patients rated their expectancies for CCT and CPT prior to randomization. RESULTS: Patients reported greater expectancy for CCT than CPT. Lower patient expectancy was associated with lower global cognition at baseline and older age. Expectancy did not differ by sex or race. There was no association between expectancy and measures of everyday functioning, hippocampus volume, or apolipoprotein E genotype. Expectancy was not associated with change in measures of global cognition, everyday functioning, and hippocampus volume from baseline to week 78, nor did expectancy interact with treatment condition. CONCLUSIONS: While greater cognitive impairment and increased age was associated with low expectancy of improvement, expectancy was not associated with the likelihood of response to treatment with CPT or CCT.

Computerized Cognitive Training in Mild Cognitive Impairment: Findings in African Americans and Caucasians.

BACKGROUND: African Americans with MCI may be at increased risk for dementia compared to Caucasians. The effect of race on the efficacy of cognitive training in MCI is unclear. METHODS: We used data from a two-site, 78-week randomized trial of MCI comparing intensive, home-based, computerized training with Web-based cognitive games or Web-based crossword puzzles to examine the effect of race on outcomes. The study outcomes were changes from baseline in cognitive and functional scales as well as MRI-measured changes in hippocampal volume and cortical thickness. Analyses used linear models adjusted for baseline scores. This was an exploratory study. RESULTS: A total of 105 subjects were included comprising 81 whites (77.1%) and 24 African Americans (22.8%). The effect of race on the change from baseline in ADAS-Cog-11 was not significant. The effect of race on change from baseline to week 78 in the Functional Activities Questionnaire (FAQ) was significant with African American participants' FAQ scores showing greater improvements at weeks 52 and 78 (P = 0.009, P = 0.0002, respectively) than white subjects. Within the CCT cohort, FAQ scores for African American participants showed greater improvement between baseline and week 78, compared to white participants randomized to CCT (P = 0.006). There was no effect of race on the UPSA. There was no effect of race on hippocampal or cortical thickness outcomes. CONCLUSIONS: Our preliminary findings suggest that web-based cognitive training programs may benefit African Americans with MCI at least as much as Caucasians, and highlight the need to further study underrepresented minorities in AD prevention trials. (Supported by the National Institutes of Health, National Institute on Aging; ClinicalTrials.gov number, NCT03205709.).

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Erratum to: Blueberry Supplementation Effects on Neuronal and Pathological Biomarkers in Subjects at Risk for Alzheimer's Disease: A Pilot Study.

[This corrects the article DOI: 10.14283/jarlife.2023.13.].

Blueberry Supplementation Effects on Neuronal and Pathological Biomarkers in Subjects at Risk for Alzheimer's Disease: A Pilot Study.

Background: There is a need to develop non-invasive practical lifestyle interventions for preventing Alzheimer's disease (AD) in people at risk, such as those with mild cognitive impairment (MCI). Blueberry consumption has been associated with reduced risk of dementia in some epidemiologic studies and with improvements in cognition in healthy aging adults. Blood-based biomarkers have emerged at the forefront of AD therapeutics research spurred by the development of reliable ultra-sensitive "single-molecule array" assays with 100-1000-fold greater sensitivity over traditional platforms. Objective: The purpose of this study was to examine the effect of blueberry supplementation in MCI on six blood biomarkers: amyloid-beta 40 (Aß40), amyloid-beta 42 (Aß42), phosphorylated Tau181 (ptau181), neurofilament light (NfL), Glial Fibrillary acidic protein (GFAP), and Brain-Derived Neurotrophic Factor (BDNF). Methods: This was a 12-week, open-label, pilot trial of 10 participants with MCI (mean age 80.2 years + 5.16). Subjects consumed 36 grams per day of lyophilized blueberry powder in a split dose consumed with breakfast and dinner. Baseline and endpoint venous blood was analyzed using an ultrasensitive SIMOA assay. Our aim was to test if blueberry supplementation would particularly impact p-tau181, NfL, and GFAP elevations associated with the neurodegenerative process. Results: There were no statistically significant (p < 0.05) changes from baseline to endpoint for any of the biomarker values or in the ratios of Aß42 / Aß40 and ptau181/ Aß42. Adverse effects were mild and transient; supplementation was relatively well tolerated with all subjects completing the study. Conclusion: To our knowledge, this is the first study to prospectively examine the effects of blueberry supplementation on a panel of blood biomarkers reflecting the neurodegenerative process. Our findings raise two possibilities - a potential stabilization of the neurodegenerative process or a lack of a direct and acute effect on beta-amyloid/tau/glial markers. A larger controlled study is warranted.

Enhancing social connectedness with companion robots using AI.

Companion robots with AI may usher a new science of social connectedness that requires the development of ethical frameworks.

Reproductive Markers in Alzheimer's Disease Progression: The Framingham Heart Study.

BACKGROUND: Reproductive status, such as the age of menarche or menopause, may be linked to cognitive abilities and risk for incident Alzheimer's disease (AD) but the evidence is conflicting. It is also not fully known if these factors interact with cortical beta-amyloid deposition. OBJECTIVES: To study the relationship between reproductive risks, sex hormone markers and risk for decline in specific cognitive domains in women. DESIGN, SETTING AND PARTICIPANTS: We analyzed the association of reproductive markers (age at menarche, number of births, age at menopause, sex hormone-binding globulin, estradiol, estrone, total testosterone, free testosterone) with incident AD and annualized cognitive decline in the community-based longitudinal Framingham Heart Study (FHS) Offspring women 60 years and older (n=772, mean age 68 years, mean follow-up 10.7 ± 3 years). We used the Cox proportional hazards regression model and linear regression model, adjusting for covariates. OUTCOME MEASURES: Incident AD dementia as well as the annualized change in memory, language, attention and executive functions. RESULTS: Older age at menopause was associated with a lower risk of incident AD dementia (p = 0.047, 6% lower risk per older year) after adjusting for baseline age, education, hormone therapy status, and MMSE score. Older age at menopause was significantly associated with a slower annualized decline in memory (beta = 0.085, p = 0.00059). The lower level of plasma Aß42 was also associated with a higher risk of incident AD (HR = 0.97, 95% CI = 0.95, 0.99; p = 0.0039) but there was no significant interaction effect with age at menarche, age at menopause or plasma sex hormone levels. CONCLUSION: Younger age at menopause is a risk factor for late-life memory decline and incident AD. This risk appears to be independent of Aß42 pathology. Further studies to understand the biological and social mechanisms underlying the differential effects of reproductive risks are warranted.

Expanding the scope of health disparities research in Alzheimer's disease and related dementias: Recommendations from the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" Workshop Series.

Topics discussed at the "Leveraging Existing Data and Analytic Methods for Health Disparities Research Related to Aging and Alzheimer's Disease and Related Dementias" workshop, held by Duke University and the Alzheimer's Association with support from the National Institute on Aging, are summarized.  Ways in which existing data resources paired with innovative applications of both novel and well-known methodologies can be used to identify the effects of multi-level societal, community, and individual determinants of race/ethnicity, sex, and geography-related health disparities in Alzheimer's disease and related dementia are proposed.  Current literature on the population analyses of these health disparities is summarized with a focus on identifying existing gaps in knowledge, and ways to mitigate these gaps using data/method combinations are discussed at the workshop.  Substantive and methodological directions of future research capable of advancing health disparities research related to aging are formulated.

Impact of Computerized Cognitive Training on Default Mode Network Connectivity in Subjects at Risk for Alzheimer's Disease: A 78-week Randomized Controlled Trial.

BACKGROUND: Mild cognitive impairment (MCI) represents a high risk group for Alzheimer's disease (AD). Computerized Cognitive Games Training (CCT) is an investigational strategy to improve targeted functions in MCI through the modulation of cognitive networks. OBJECTIVE: The goal of this study was to examine the effect of CCT versus a non-targeted active brain exercise on functional cognitive networks. METHODS: 107 patients with MCI were randomized to CCT or web-based crossword puzzles. Resting-state functional MRI (fMRI) was obtained at baseline and 18 months to evaluate differences in fMRI measured within- and between-network functional connectivity (FC) of the default mode network (DMN) and other large-scale brain networks: the executive control, salience, and sensorimotor networks. RESULTS: There were no differences between crosswords and games in the primary outcome, within-network DMN FC across all subjects. However, secondary analyses suggest differential effects on between-network connectivity involving the DMN and SLN, and within-network connectivity of the DMN in subjects with late MCI. Paradoxically, in both cases, there was a decrease in FC for games and an increase for the crosswords control (p < 0.05), accompanied by lesser cognitive decline in the crosswords group. CONCLUSION: Results do not support a differential impact on within-network DMN FC between games and crossword puzzle interventions. However, crossword puzzles might result in cognitively beneficial remodeling between the DMN and other networks in more severely impaired MCI subjects, parallel to the observed clinical benefits.

Cerebral venous biomarkers and veno-arterial gradients: untapped resources in Alzheimer's disease.

Blood based biomarkers (BBB) derived from forearm veins for estimating brain changes is becoming ubiquitous in Alzheimer's Disease (AD) research and could soon become standard in routine clinical diagnosis. However, there are many peripheral sources of contamination through which concentrations of these metabolites can be raised or lowered after leaving the brain and entering the central venous pool. This raises the issue of potential false conclusions that could lead to erroneous diagnosis or research findings. We propose the use of simultaneous sampling of internal jugular venous and arterial blood to calculate veno-arterial gradient, which can reveal either a surplus or a deficit of metabolites exiting the brain. Methods for sampling internal jugular venous and arterial blood are described along with examples of the use of the veno-arterial gradient in non-AD brain research. Such methods in turn could help better establish the accuracy of forearm venous biomarkers.