Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition.

BACKGROUND: The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed. METHODS: We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition. RESULTS: Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity. CONCLUSIONS: Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.

Cognitive, emotional, and social factors promoting psychosocial adaptation: a study of latent profiles in people living in socially vulnerable contexts.

Introduction: Social adaptation is a multifaceted process that encompasses cognitive, social, and affective factors. Previous research often focused on isolated variables, overlooking their interactions, especially in challenging environments. Our study addresses this by investigating how cognitive (working memory, verbal intelligence, self-regulation), social (affective empathy, family networks, loneliness), and psychological (locus of control, self-esteem, perceived stress) factors interact to influence social adaptation. Methods: We analyzed data from 254 adults (55% female) aged 18 to 46 in economically vulnerable households in Santiago, Chile. We used Latent profile analysis (LPA) and machine learning to uncover distinct patters of socioadaptive features and identify the most discriminating features. Results: LPA showed two distinct psychosocial adaptation profiles: one characterized by effective psychosocial adaptation and another by poor psychosocial adaptation. The adaptive profile featured individuals with strong emotional, cognitive, and behavioral self-regulation, an internal locus of control, high self-esteem, lower stress levels, reduced affective empathy, robust family support, and decreased loneliness. Conversely, the poorly adapted profile exhibited the opposite traits. Machine learning pinpointed six key differentiating factors in various adaptation pathways within the same vulnerable context: high self-esteem, cognitive and behavioral self-regulation, low stress levels, higher education, and increased social support. Discussion: This research carries significant policy implications, highlighting the need to reinforce protective factors and psychological resources, such as self-esteem, self-regulation, and education, to foster effective adaptation in adversity. Additionally, we identified critical risk factors impacting social adaptation in vulnerable populations, advancing our understanding of this intricate phenomenon.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling.

INTRODUCTION: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS: We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS: Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION: The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

Neurocognitive correlates of semantic memory navigation in Parkinson's disease.

Cognitive studies on Parkinson's disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients' neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.g., 'sun') and list their features (e.g., hot). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.

The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds.

The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer's disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson's disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21-89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.

Altered Sleep Microarchitecture and Cognitive Impairment in Patients With Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: To investigate the spatiotemporal characteristics of sleep waveforms in temporal lobe epilepsy (TLE) and examine their association with cognition. METHODS: In this retrospective, cross-sectional study, we examined overnight EEG data from adult patients with TLE and nonepilepsy comparisons (NECs) admitted to the epilepsy monitoring unit at Mass General Brigham hospitals. Automated algorithms were used to characterize sleep macroarchitecture (sleep stages) and microarchitecture (spindles, slow oscillations [SOs]) on scalp EEG and to detect hippocampal interictal epileptiform discharges (hIEDs) from foramen ovale electrodes simultaneously recorded in a subset of patients with TLE. We examined the association of sleep features and hIEDs with memory and executive function from clinical neuropsychological evaluations. RESULTS: A total of 81 adult patients with TLE and 28 NEC adult patients were included with similar mean ages. There were no significant differences in sleep macroarchitecture between groups, including relative time spent in each sleep stage, sleep efficiency, and sleep fragmentation. By contrast, the spatiotemporal characteristics of sleep microarchitecture were altered in TLE compared with NEC and were associated with cognitive impairments. Specifically, we observed a ∼30% reduction in spindle density in patients with TLE compared with NEC, which was significantly associated with worse memory performance. Spindle-SO coupling strength was also reduced in TLE and, in contrast to spindles, was associated with diminished executive function. We found no significant association between sleep macroarchitectural and microarchitectural parameters and hIEDs. DISCUSSION: There is a fundamental alteration of sleep microarchitecture in TLE, characterized by a reduction in spindle density and spindle-SO coupling, and these changes may contribute to neurocognitive comorbidity in this disorder.

Factors associated with healthy aging in Latin American populations.

Latin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models (n = 44,394 participants). Risk factors associated with social and health disparities, including SDH (ß > 0.3), mental health (ß > 0.6) and cardiometabolic risks (ß > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: ß < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions.

The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations.

Large variability exists across brain regions in health and disease, considering their cellular and molecular composition, connectivity, and function. Large-scale whole-brain models comprising coupled brain regions provide insights into the underlying dynamics that shape complex patterns of spontaneous brain activity. In particular, biophysically grounded mean-field whole-brain models in the asynchronous regime were used to demonstrate the dynamical consequences of including regional variability. Nevertheless, the role of heterogeneities when brain dynamics are supported by synchronous oscillating state, which is a ubiquitous phenomenon in brain, remains poorly understood. Here, we implemented two models capable of presenting oscillatory behavior with different levels of abstraction: a phenomenological Stuart-Landau model and an exact mean-field model. The fit of these models informed by structural- to functional-weighted MRI signal (T1w/T2w) allowed us to explore the implication of the inclusion of heterogeneities for modeling resting-state fMRI recordings from healthy participants. We found that disease-specific regional functional heterogeneity imposed dynamical consequences within the oscillatory regime in fMRI recordings from neurodegeneration with specific impacts on brain atrophy/structure (Alzheimer's patients). Overall, we found that models with oscillations perform better when structural and functional regional heterogeneities are considered, showing that phenomenological and biophysical models behave similarly at the brink of the Hopf bifurcation.

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization.

Introduction: Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods: We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results: Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion: Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.

Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases.

Aging may diminish social cognition, which is crucial for interaction with others, and significant changes in this capacity can indicate pathological processes like dementia. However, the extent to which non-specific factors explain variability in social cognition performance, especially among older adults and in global settings, remains unknown. A computational approach assessed combined heterogeneous contributors to social cognition in a diverse sample of 1063 older adults from 9 countries. Support vector regressions predicted the performance in emotion recognition, mentalizing, and a total social cognition score from a combination of disparate factors, including clinical diagnosis (healthy controls, subjective cognitive complaints, mild cognitive impairment, Alzheimer's disease, behavioral variant frontotemporal dementia), demographics (sex, age, education, and country income as a proxy of socioeconomic status), cognition (cognitive and executive functions), structural brain reserve, and in-scanner motion artifacts. Cognitive and executive functions and educational level consistently emerged among the top predictors of social cognition across models. Such non-specific factors showed more substantial influence than diagnosis (dementia or cognitive decline) and brain reserve. Notably, age did not make a significant contribution when considering all predictors. While fMRI brain networks did not show predictive value, head movements significantly contributed to emotion recognition. Models explained between 28-44% of the variance in social cognition performance. Results challenge traditional interpretations of age-related decline, patient-control differences, and brain signatures of social cognition, emphasizing the role of heterogeneous factors. Findings advance our understanding of social cognition in brain health and disease, with implications for predictive models, assessments, and interventions.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

Social and non-social working memory in neurodegeneration.

Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.

Model-based whole-brain perturbational landscape of neurodegenerative diseases.

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.

BACKGROUND: Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. METHODS: We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. FINDINGS: Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. INTERPRETATION: The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. FUNDING: The specific funding of this article is provided in the acknowledgements section.

The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations.

Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.

Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease.

Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels.SIGNIFICANCE STATEMENT By assessing the irreversibility of large-scale dynamics across multiple brain signals, we provide a precise signature capable of distinguishing Alzheimer's disease (AD) at the global, local, and network levels and different oscillatory regimes. Irreversibility of limbic, frontoparietal, default-mode, and salience networks was the most compromised by AD compared with more sensory-motor networks. Moreover, the time-irreversibility properties associated with cognitive decline and atrophy outperformed and complemented classical neurocognitive markers of AD in predictive classification performance. Findings were generalized and replicated with an out-of-sample validation procedure. We provide novel multilevel evidence of reduced irreversibility in AD brain dynamics that has the potential to open new avenues for understating neurodegeneration in terms of the temporal asymmetry of brain dynamics.

Source space connectomics of neurodegeneration: One-metric approach does not fit all.

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. OBJECTIVE: To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. METHODS: We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. RESULTS: SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. CONCLUSION: SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.