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Abstract: There is considerable empirical evidence that unequivocally points to loneliness as a modifiable risk factor for the development of Alzheimer's disease and other related dementias. With the emergence of the COVID-19 pandemic and the resulting lockdown and social distancing, there has been a renewed interest in studying this topic. The present review examines the links between loneliness and Alzheimer's disease, with particular emphasis on the mechanisms common to both conditions.

We study how brain volume loss at old age is affected by factors such as age, APOE gene, sex, and school level. The quantitative characterization of brain volume loss at old age relative to young age requires at least in principle two MRI scans performed at both young and old age. There is, however, a way to address the problem by having only one MRI scan at old age. We compute the total brain loss of elderly subjects as the ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 were assessed. The causal analysis of factors affecting brain atrophy was performed using Probabilistic Bayesian Modeling and the Mathematics of Causal Inference. We find that healthy subjects get into their seventies with an average brain volume loss of 30% from their maximum brain volume at a young age. Both age and the sexes are causally related to brain atrophy, with women getting to elderly age with 1% larger brain volume relative to intracranial volume than men. How the brain ages and what are the reasons for sex differences in adult lifespan are causal questions that need to be addressed with causal inference and empirical data. The graphical causal modeling presented here can be instrumental in understanding a puzzling scientific inquiry -the biological age of the brain.

Here, we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigated whether subcortical volume variations can be explained by the adjacency of structures in the same hemisphere or are due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in each hemisphere were automatically segmented in a large sample of 3312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We performed Eigenvalue analysis, and found that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency whether considered in the same hemisphere (intrahemispherically) or different hemispheres (interhemispherically). Our results indicate that anatomic bilaterality of subcortical volumes is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent subcortical structures might act as a mechanism to compensate for the deleterious effects of aging.

INTRODUCTION The theoretical framework of the Alzheimer's disease continuum considers transition between stages in a unidirectional manner. Here we examine the rate of reversion from mild cognitive impairment (MCI) to normal cognition (NC) and explore a set of potential variables associated with this phenomenon. METHODS A total of 985 Spanish community-dwelling individuals aged 70 years and over at baseline were monitored for 5 years. During this time, 173 MCI and 36 dementia cases were identified. Multi-state Markov models were performed to characterize transitions between states through the dementia continuum. RESULTS The rate of reversion from MCI to NC was 11%. There were significant non-modifiable (age, socioeconomic status, or apolipoprotein E) and modifiable factors (cognitive training or absence of affective symptoms) associated with reversion. DISCUSSION Overall, our results highlight that the likelihood of progression from MCI to dementia is very similar to that of reversion from MCI to NC.

In recent years there has been increasing interest in examining the role of empathic abilities in Alzheimer's disease (AD). Empathy, the ability to understand and share another person's feelings, implies the existence of emotional and cognitive processes and is a pivotal aspect for success in social interactions. In turn, self-empathy is oriented to one's thoughts and feelings. Decline of empathy and self-empathy can occur during the AD continuum and can be linked to different neuroanatomical pathways in which the cingulate cortex may play a crucial role. Here, we will summarize the involvement of empathic abilities through the AD continuum and further discuss the potential neurocognitive mechanisms that contribute to decline of empathy and self-empathy in AD., Spanish Ministry of Science, Innovation and Universities –State Research Agency– under the Grant RTI2018-098762-A-C32 by the European Regional Development Fund. Complimentary, it was supported by the multidisciplinary Research Program of the Fundación General de la Universidad de Salamanca (FGUSAL) in the framework of the Centro Internacional sobre el Envejecimiento (CENIE)

In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance imaging (MRI) scans collected for this study. We find a lack of linear correlation between the segmentation volume estimates obtained from FreeSurfer and FSL. On the other hand, FreeSurfer volume estimates tend to be larger thanFSL estimates of the areas putamen, thalamus, amygdala, caudate, pallidum, hippocampus, and accumbens. The characterization of the performance of brain segmentation algorithms in large datasets as the one presented here is a necessary step towards partially or fully automated end-to-end neuroimaging workflow both in clinical and research settings.

Here, we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigated whether subcortical volume variations can be explained by the adjacency of structures in the same hemisphere or are due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in each hemisphere were automatically segmented in a large sample of 3312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We performed Eigenvalue analysis, and found that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency whether considered in the same hemisphere (intrahemispherically) or different hemispheres (interhemispherically). Our results indicate that anatomic bilaterality of subcortical volumes is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent subcortical structures might act as a mechanism to compensate for the deleterious effects of aging.

The progressive aging of the population represents a challenge for society. In particular, a strong increase in the number of people over 90 is expected in the next two decades. As this phenomenon will lead to an increase in illness and age-related dependency, the study of long-lived people represents an opportunity to explore which lifestyle factors are associated with healthy aging and which with the emergence of age-related diseases, especially Alzheimer’s type dementia. The project “Factors associated with healthy and pathologically aging in a sample of elderly people over 90 in the city of Madrid” (MADRID+90) brings together a multidisciplinary research team in neurodegenerative diseases that includes experts in epidemiology, neurology, neuropsychology, neuroimaging and computational neuroscience. In the first phase of the project, a stratified random sampling was carried out according to the census of the city of Madrid followed by a survey conducted on 191 people aged 90 and over. This survey gathered information on demographics, clinical data, lifestyles and cognitive status. Here, the main results of that survey are showed. The second phase of the project aims to characterize individual trajectories in the course of either healthy and pathological aging, from a group of 50 subjects over 90 who will undergo a comprehensive clinical examination comprised of neurological and cognitive testing, MRI and EEG. The ultimate goal of the project is to characterize the biophysical and clinical profiles of a population that tends to receive little attention in the literature. A better understanding of the rapidly increasing group of nonagenarians will also help to design new policies that minimize the impact and future social and economic consequences of rapidly aging societies.

The progressive ageing of the population represents a challenge for society. In particular, a strong increase in the number of people over 90 is expected in the next two decades. As this phenomenon will lead to an increase in illness and age-related dependency, the study of long-lived people represents an opportunity to explore which lifestyle factors are associated with healthy ageing and which with the emergence of age-related diseases, especially Alzheimer's type dementia. The project “Factors associated with healthy and pathologically aging in a sample of elderly people over 90 in the city of Madrid” (MADRID+90) brings together a powerful multidisciplinary research team in neurodegenerative diseases that includes experts in epidemiology, neurology, neuropsychology, neuroimaging and computational neuroscience. In the first phase of the project, a stratified random sampling was carried out according to the census of the city of Madrid followed by a survey conducted on 191 people aged 90 and over. This survey gathered information on demographics, clinical data, lifestyles and cognitive status. Here, the main results of that survey are showed. The second phase of the project aims to characterize individual trajectories in the course of either healthy and pathological ageing, from a group of 50 subjects over 90 who will undergo a comprehensive clinical examination comprised of neurological and cognitive testing, MRI and EEG. The ultimate goal of the project is to characterize the biophysical and clinical profiles of a population that tends to receive little attention in the literature. A better understanding of the rapidly increasing group of nonagenarians will also help to design new policies that minimize the impact and future social and economic consequences of the ageing phenomenon.

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state to investigate the alpha desynchronization hypothesis. The change in functional connectivity from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We perform network connectivity analysis in iEEG and we find that phase-based connectivity is sensitive to the transition from eyes closed to eyes open only in interhemispheral and frontal electrodes. Power based connectivity, on the other hand, consistently discriminates between the two conditions in temporal and interhemispheral electrodes. Additionally, we provide a calculation for the wiring cost, defined in terms of the connectivity between electrodes weighted by distance. We find that the wiring cost variation from eyes closed to eyes open is sensitive to the eyes closed and eyes open conditions. We extend the standard network-based approach using the filtration method from algebraic topology which does not rely on the threshold selection problem. Both the wiring cost measure defined here and this novel methodology provide a new avenue for understanding the electrophysiology of resting state.

Alzheimer’s Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. are called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study we leverage onThe Vallecas Project, a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

IMPORTANCE: There is strong evidence about the association between low socioeconomic status (SES) and higher risk of dementia. However, it has not been conveniently addressed so far the role of SES on the incidence of Mild Cognitive Impairment (MCI) which is considered a prodromal stage of latter dementia.OBJECTIVE: To examine the impact of individual and neighborhood dimensions of SES, as well as their interaction, on the risk of developing MCI in a sample of older adults that were followed-up for seven years.DESIGN, SETTING, AND PARTICIPANTS: Data from the Vallecas Project cohort, an ongoing community-based longitudinal study for early detection of cognitive impairment and dementia, were used to build two indices of SES namely individual and neighborhood, as well as a global SES as a combination of both, and to investigate their effects on MCI conversion by means of a multivariate-adjusted Cox proportional hazard model.MAIN OUTCOMES AND MEASURES: Based on data referred to as individual-level (one´s educational attainment, occupation, parent´s level of education) and neighborhood-level (district´s income, unemployment rate, housing price, percentage of people with no formal qualifications, with higher education, and with white-collar employs) different composite measures of SES were built and quartiles were considered for further analyses. MCI diagnoses were agreed between neurologists and neuropsychologists at consensus meetings.RESULTS: 1180 participants aged 70 years and older were enrolled in this study (mean age at baseline, 74.9 years; 63.5% females). Of these, 199 cases of MCI (16.9%) were diagnosed at any point of the follow-up. The individual and neighborhood dimensions of SES played different roles in the dynamics of the MCI occurrence through ageing. Most importantly, the risk of developing MCI was almost double for lower SES quartiles when compared to the highest one.CONCLUSIONS AND RELEVANCE: The incidence of MCI in older adults was related to both individual characteristics and socioeconomic context. Public health strategies should be holistic and focus not only on promoting the classical individual preventive measures, but also on reducing social inequalities as a means of fostering healthy ageing and reducing dementia burden.

Alzheimer’s Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in early stages of the disease is a paramount obstacle to formulate a preclinical and predictive model of AD. Not surprisingly, the AD drug approval rate is one of the lowest in the industry, an exiguous 0.4%. The identification of risk factors, preferably obtained by the subject herself, is sorely needed given that the incidence of Alzheimer’s disease grows exponentially with age [Ferri et al., 2005], [Ganguli and Rodriguez, 2011].During the last 7 years, researchers at Proyecto Vallecas have collected information about the project’s volunteers, aged 70 or more. The Proyecto Vallecas dataset includes information about a wide range of factors including magnetic resonance imaging, genetic, demographic, socioeconomic, cognitive performance, subjective memory complaints, neuropsychiatric disorders, cardiovascular, sleep, diet, physical exercise and self assessed quality of life. The subjects in each visit were diagnosed as healthy, mild cognitive impairment (MCI) or dementia.In this study we perform Exploratory Data Analysis to summarize the main characteristics of this unique longitudinal dataset. The objective is to characterize the evolution of the collected features over time and most importantly, how their dynamics are related to cognitive decline. We show that the longitudinal dataset of Proyecto Vallecas, if conveniently exploited, holds promise to identifying either factors promoting healthy aging and risk factors related to cognitive decline.

Sleep plays substantial role in daily cognitive performance, mood and memory. The study of sleep has attracted the interest of neuroscientists, clinicans and the overall population, with increasing number of adults suffering from insufficient amounts of sleep. Sleep is an activity composed of different stages whose temporal dynamics, cycles and inter dependencies are not fully understood. Healthy body function and personal well being, however, depends on proper unfolding and continuance of the sleep cycles. The characterization of the different sleep stages can be undertaken with the development of biomarkers derived from sleep recording. For this purpose, in this work we analyzed single-channel EEG signals from 106 healthy subjects. The signals were quantified using the permutation vector approach using five different information theoretic measures: i) Shannon’s entropy, ii) MPR statistical complexity, iii) Fisher information, iv) Renyí Min-entropy and v) Lempel-Ziv complexity. The results show that all five information theory-based measures make possible to quantify and classify the underlying dynamics of the different sleep stages. In addition to this, we combine these measures to show that planes containing pairs of measures, such as the plane composed of Lempel-Ziv and Shannon, have a better performance for differentiating sleep states than measures used individually for the same purpose.

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state, and show that functional connectivity in the alpha band decreases in the eyes open condition compared to eyes closed. This "alpha desynchronization " or reduction in the number of connections from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We provide two calculations of the wiring cost, local and mesoscopic, defined in terms of the distance between the electrodes and the likelihood that they are functionally connected. We find that, in agreement with the "alpha desynchronization" hypothesis, the local wiring cost decreases going from eyes closed to eyes open. However, when the wiring cost calculation takes into account the connectivity pattern, the wiring cost variation from eyes closed to eyes open is not as consistent and shows regional specificity. The wiring cost measure defined here, provides a new avenue for understanding the electrophysiology of resting state.

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