Symptoms In Mild Cognitive Impairment Research Articles

Association of Sleep Electroencephalography-Based Brain Age Index With Dementia

Dementia is an increasing cause of disability and loss of independence in the elderly population yet remains largely underdiagnosed. A biomarker for dementia that can identify individuals with or at risk for developing dementia may help close this diagnostic gap. To investigate the association between a sleep electroencephalography-based brain age index (BAI), the difference between chronological age and brain age estimated using the sleep electroencephalogram, and dementia. In this retrospective cross-sectional study of 9834 polysomnograms, BAI was computed among individuals with previously determined dementia, mild cognitive impairment (MCI), or cognitive symptoms but no diagnosis of MCI or dementia, and among healthy individuals without dementia from August 22, 2008, to June 4, 2018. Data were analyzed from November 15, 2018, to June 24, 2020. Dementia, MCI, and dementia-related symptoms, such as cognitive change and memory impairment. The outcome measures were the trend in BAI when moving from groups ranging from healthy, to symptomatic, to MCI, to dementia and pairwise comparisons of BAI among these groups. A total of 5144 sleep studies were included in BAI examinations. Patients in these studies had a median (interquartile range) age of 54 (43-65) years, and 3026 (59%) were men. The patients included 88 with dementia, 44 with MCI, 1075 who were symptomatic, and 2336 without dementia. There was a monotonic increase in mean (SE) BAI from the nondementia group to the dementia group (nondementia: 0.20 [0.42]; symptomatic: 0.58 [0.41]; MCI: 1.65 [1.20]; dementia: 4.18 [1.02]; P < .001). These findings suggest that a sleep-state electroencephalography-based BAI shows promise as a biomarker associated with progressive brain processes that ultimately result in dementia.

AD risk score for the early phases of disease based on unsupervised machine learning.

Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention. An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort. Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum. The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.

Cognitive reserve and midlife vascular risk: Cognitive and clinical outcomes.

ObjectiveExamine whether cognitive reserve moderates the association of 1) vascular risk factors and 2) white matter hyperintensity burden with risk of clinical progression and longitudinal cognitive decline.MethodsBIOCARD Study participants were cognitively normal and primarily middle‐aged (M = 57 years) at baseline and have been followed with annual cognitive and clinical assessments (M = 13 years). Baseline cognitive reserve was indexed with a composite score combining education with reading and vocabulary scores. Baseline vascular risk (N = 229) was assessed with a composite risk score reflecting five vascular risk factors. Baseline white matter hyperintensity load (N = 271) was measured with FLAIR magnetic resonance imaging. Cox regression models assessed risk of progression from normal cognition to onset of clinical symptoms of Mild Cognitive Impairment. Longitudinal mixed effects models measured the relationship of these variables to cognitive decline, using a global composite score, and executive function and episodic memory sub‐scores.ResultsBoth vascular risk and white matter hyperintensities were associated with cognitive decline, particularly in executive function. Higher vascular risk, but not white matter hyperintensity burden, was associated with an increased risk of progression to Mild Cognitive Impairment. Higher cognitive reserve was associated with a reduced risk of symptom onset and higher levels of baseline cognition but did not modify the associations between the vascular risk score and white matter hyperintensities with clinical progression or cognitive decline.InterpretationAlthough cognitive reserve has protective effects on clinical and cognitive outcomes, it does not mitigate the negative impact of vascular risk and small vessel cerebrovascular disease on these same outcomes.

Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment.

We applied artificial intelligence techniques to build correlate models that predict general poor health in a national sample of caregivers with mild cognitive impairment (MCI). Our application of deep learning identified age, duration of caregiving, amount of alcohol intake, weight, myocardial infarction (MI) and frequency of MCI symptoms for Blacks and Hispanics whereas frequency of MCI symptoms, income, weight, coronary heart disease (CHD), age, and use of e-cigarette for the others as the strongest correlates of poor health among 81 variables entered. The application of artificial intelligence efficiently provided intervention strategies for Black and Hispanic caregivers with MCI.

Mild Cognitive Impairment Staging Yields Genetic Susceptibility, Biomarker, and Neuroimaging Differences.

IntroductionWhile Alzheimer’s disease (AD) is divided into severity stages, mild cognitive impairment (MCI) remains a solitary construct despite clinical and prognostic heterogeneity. This study aimed to characterize differences in genetic, cerebrospinal fluid (CSF), neuroimaging, and neuropsychological markers across clinician-derived MCI stages.MethodsVanderbilt Memory & Aging Project participants with MCI were categorized into 3 severity subtypes at screening based on neuropsychological assessment, functional assessment, and Clinical Dementia Rating interview, including mild (n = 18, 75 ± 8 years), moderate (n = 89 72 ± 7 years), and severe subtypes (n = 18, 78 ± 8 years). At enrollment, participants underwent neuropsychological testing, 3T brain magnetic resonance imaging (MRI), and optional fasting lumbar puncture to obtain CSF. Neuropsychological testing and MRI were repeated at 18-months, 3-years, and 5-years with a mean follow-up time of 3.3 years. Ordinary least square regressions examined cross-sectional associations between MCI severity and apolipoprotein E (APOE)-ε4 status, CSF biomarkers of amyloid beta (Aβ), phosphorylated tau, total tau, and synaptic dysfunction (neurogranin), baseline neuroimaging biomarkers, and baseline neuropsychological performance. Longitudinal associations between baseline MCI severity and neuroimaging and neuropsychological trajectory were assessed using linear mixed effects models with random intercepts and slopes and a follow-up time interaction. Analyses adjusted for baseline age, sex, race/ethnicity, education, and intracranial volume for MRI models.ResultsStages differed at baseline on APOE-ε4 status (early < middle = late; p-values < 0.03) and CSF Aβ (early > middle = late), phosphorylated and total tau (early = middle < late; p-values < 0.05), and neurogranin concentrations (early = middle < late; p-values < 0.05). MCI stage related to greater longitudinal cognitive decline, hippocampal atrophy, and inferior lateral ventricle dilation (early < late; p-values < 0.03).DiscussionClinician staging of MCI severity yielded longitudinal cognitive trajectory and structural neuroimaging differences in regions susceptible to AD neuropathology and neurodegeneration. As expected, participants with more severe MCI symptoms at study entry had greater cognitive decline and gray matter atrophy over time. Differences are likely attributable to baseline differences in amyloidosis, tau, and synaptic dysfunction. MCI staging may provide insight into underlying pathology, prognosis, and therapeutic targets.

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

AbstractAlzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.

Is Blood Brain-Derived Neurotrophic Factor a Useful Biomarker to Monitor Mild Cognitive Impairment Patients?

Availability of reliable prognostic biomarkers that are also able to monitor preventive/therapeutic interventions in patients with mild cognitive impairment (MCI) is crucial. Cerebral brain-derived neurotrophic factor (BDNF) alterations were evidenced in Alzheimer's disease, but the value of blood BDNF in MCI is unclear, especially because of the incomplete/incorrect management of the numerous confounding factors unrelated to the disease. This study, applying a multidisciplinary methodological approach, aimed at clarifying whether blood BDNF can really mirror the cognitive symptoms of MCI, thus supporting the evaluation of clinical protocols' effectiveness as well as the definition of the conversion rate to dementia. Healthy elderly subjects (HE) and MCI patients were assessed for sociodemographic, neuropsychological, pharmacological, and lifestyle data, and plasma BDNF was measured (baseline); then, in the MCI cohort, the biomarker was tested in a comprehensive cognitive stimulation intervention (CS) as well as in a 2-year follow-up period. Plasma BDNF, cleansed from all the interfering factors, (1) did not discriminate HE and MCI patients; (2) in MCI patients reflected mood, social engagement, and subjective memory complaints but not cognition; (3) changed due to CS, although with no correlations to cognitive performances; and (4) predicted no functional deterioration. Our data indicate that the possible biased use of plasma BDNF in MCI is critically risky.

Effects of moxibustion on symptoms of mild cognitive impairment: protocol of a systematic review and meta-analysis

IntroductionMild cognitive impairment (MCI) is considered the intermediate phase between normal age-related cognitive decline and dementia. Moxibustion has gained increased popularity for the management of MCI in China.This study aimed...

Prefrontal functional connectivity analysis of cognitive decline for early diagnosis of mild cognitive impairment: a functional near-infrared spectroscopy study.

Cognitive decline (CD) is a major symptom of mild cognitive impairment (MCI). Patients with MCI have an increased likelihood of developing Alzheimer's disease (AD). Although a cure for AD is currently lacking, medication therapies and/or daily training in the early stage can alleviate disease progression and improve patients' quality of life. Accordingly, investigating CD-related biomarkers via brain imaging devices is crucial for early diagnosis. In particular, "portable" brain imaging devices enable frequent diagnostic checks as a routine clinical tool, and therefore increase the possibility of early AD diagnosis. This study aimed to comprehensively investigate functional connectivity (FC) in the prefrontal cortex measured by a portable functional near-infrared spectroscopy (fNIRS) device during a working memory (WM) task known as the delayed matching to sample (DMTS) task. Differences in prefrontal FC between healthy control (HC) (n = 23) and CD groups (n = 23) were examined. Intra-group analysis (one-sample t-test) revealed significantly greater prefrontal FC, especially left- and inter-hemispheric FC, in the CD group than in the HC. These observations could be due to a compensatory mechanism of the prefrontal cortex caused by hippocampal degeneration. Inter-group analysis (unpaired two-sample t-test) revealed significant intergroup differences in left- and inter-hemispheric FC. These attributes may serve as a novel biomarker for early detection of MCI. In addition, our findings imply that portable fNIRS devices covering the prefrontal cortex may be useful for early diagnosis of MCI.

Phasic alerting enhances spatial orienting in healthy aging but not in mild cognitive impairment.

Previous studies in young adults have demonstrated strong functional links between phasic alerting and exogenous orienting. The present study examined changes in the dynamic interaction between these attentional networks in healthy aging and in amnestic mild cognitive impairment. Healthy young adults, healthy older adults, and patients with amnestic mild cognitive impairment (MCI) were asked to identify as quickly as possible the color of a target stimulus that appeared within one of 2 peripheral boxes. Orienting was manipulated by a brief flashing of the same (valid cue) or opposite (invalid cue) box in which the target subsequently appeared. Alerting was manipulated by presenting an auditory white noise burst simultaneously with the visual orienting cue on half of the trials. All 3 groups displayed significant alerting and orienting effects but differed in the nature of the interaction between alerting and orienting. As expected, young adults displayed increased orienting under high alerting conditions through a selective enhancement of validly cued targets. While older adults displayed a greater effect of alerting on orienting compared to young adults, MCI patients did not display a significant interaction between attentional networks. Results provide support for the presence of increased compensatory interactions between attentional networks in healthy aging that may be no longer effective with the emergence of clinical symptoms in MCI. The demonstration of qualitatively distinct effects of healthy aging and MCI suggests that behavioral tests of attentional network interactions may serve as cognitive markers in individuals at increased risk for developing AD. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

White matter hyperintensities and neuropsychiatric symptoms in mild cognitive impairment and Alzheimer’s disease

Neuropsychiatric symptoms (NPS), such as apathy, irritability and depression, are frequently encountered in patients with Alzheimer’s disease (AD). Focal grey matter atrophy has been linked to NPS development. Cerebrovascular disease is common among AD patients and can be detected on MRI as white matter hyperintensities (WMH). In this longitudinal study, the relative contribution of WMH burden and GM atrophy to NPS was evaluated in a cohort of mild cognitive impairment (MCI), AD and normal controls. This study included 121 AD, 315 MCI and 225 normal control subjects from the Alzheimer’s Disease Neuroimaging Initiative. NPS were assessed using the Neuropsychiatric Inventory and grouped into hyperactivity, psychosis, affective and apathy subsyndromes. WMH were measured using an automatic segmentation technique and mean deformation-based morphometry (DBM) was used to measure atrophy of grey matter regions. Linear mixed-effects models found focal grey matter atrophy and WMH volume both contributed significantly to NPS subsyndromes in MCI and AD subjects, however, WMH burden played a greater role. This study could provide a better understanding of the pathophysiology of NPS in AD and support the monitoring and control of vascular risk factors.

Developing retinal biomarkers for the earliest stages of Alzheimer's disease: What we know, what we don't, and how to move forward.

The last decade has seen a substantial increase in research focused on the identification, development, and validation of diagnostic and prognostic retinal biomarkers for Alzheimer's disease (AD). Sensitive retinal biomarkers may be advantageous because they are cost and time efficient, non-invasive, and present a minimal degree of patient risk and a high degree of accessibility. Much of the work in this area thus far has focused on distinguishing between symptomatic AD and/or mild cognitive impairment (MCI) and cognitively normal older adults. Minimal work has been done on the detection of preclinical AD, the earliest stage of AD pathogenesis characterized by the accumulation of cerebral amyloid absent clinical symptoms of MCI or dementia. The following review examines retinal structural changes, proteinopathies, and vascular alterations that have been proposed as potential AD biomarkers, with a focus on studies examining the earliest stages of disease pathogenesis. In addition, we present recommendations for future research to move beyond the discovery phase and toward validation of AD risk biomarkers that could potentially be used as a first step in a multistep screening process for AD risk detection.

Depressive symptoms and CSF Alzheimer's disease biomarkers in relation to clinical symptom onset of mild cognitive impairment.

IntroductionWe sought to examine whether depressive symptoms and level of Alzheimer's disease (AD) pathology are independently or interactively associated with the risk of progression to mild cognitive impairment (MCI).MethodsThe study included a total of 216 participants from the Biomarkers for Older Controls at Risk for Alzheimer's Disease study, a cohort of individuals who were cognitively normal at baseline (mean age = 57) and followed for more than 20 years (mean = 12.7 years), who had baseline Hamilton Depression Scale (HAM‐D) scores and cerebrospinal fluid (CSF) amyloid beta (Aβ)1‐42 , t‐tau, and p‐tau measures available.ResultsCox regression demonstrated that baseline HAM‐D and CSF AD biomarkers were both associated with time to onset of MCI. There was an interaction between HAM‐D scores and markers of AD pathology, in which depression was associated with time of onset in participants with low levels of AD pathology (hazard ratio = 0.64; 95% confidence interval = 0.43–0.95; P= .026).DiscussionThe effect of depressive symptoms on progression to clinical symptoms of MCI may be most evident among individuals with low levels of AD pathology.

White matter hyperintensities and CSF Alzheimer disease biomarkers in preclinical Alzheimer disease.

Recent studies suggest that white matter hyperintensities (WMH) on MRI, which primarily reflect small vessel cerebrovascular disease, may play a role in the evolution of Alzheimer disease (AD). In a longitudinal study, we investigated whether WMH promote the progression of AD pathology, or alter the association between AD pathology and risk of progression from normal cognition to mild cognitive impairment (MCI). Two sets of analyses were conducted. The relationship between whole brain WMH load, based on fluid-attenuated inversion recovery MRI, obtained in initially cognitively normal participants (n = 274) and time to onset of symptoms of MCI (n = 60) was examined using Cox regression models. In a subset of the participants with both MRI and CSF data (n = 204), the interaction of WMH load and CSF AD biomarkers was also evaluated. Baseline WMH load interacted with CSF total tau (t-tau) with respect to symptom onset, but not with CSF β-amyloid 1-42 or phosphorylated tau (p-tau) 181. WMH volume was associated with time to symptom onset of MCI among individuals with low t-tau (hazard ratio [HR] 1.35, confidence interval [CI] 1.06-1.73, p = 0.013), but not those with high t-tau (HR 0.86, CI 0.56-1.32, p = 0.47). The rate of change in the CSF biomarkers over time was not associated with the rate of change in WMH volumes. These results suggest that WMH primarily affect the risk of progression when CSF measures of neurodegeneration or neuronal injury (as reflected by t-tau) are low. However, CSF biomarkers of amyloid and p-tau and WMH appear to have largely independent and nonsynergistic effects on the risk of progression to MCI.

Role of depressive symptoms in subjective memory complaint in older adults with mild cognitive impairment.

To explore the independent relationship between depressive symptoms and subjective memory complaint (SMC) amongst older adults with mild cognitive impairment (MCI) after adjusting for objective cognitive function and other important confounding factors. subjective memory complaint is a core symptom of MCI and is often the primary reason for older adults with MCI to seek for medical help. Improving subjective memory amongst older adults with MCI is important to enhance their quality of life and potentially delay further cognitive decline. Depressive symptoms, which are highly prevalent neuropsychiatric symptoms amongst older adults with MCI, may be one of the reasons that affect an individual's self-perception of memory function. However, there is a dearth of studies to provide a thorough evaluation of the independent relationship between depressive symptoms and SMC amongst older adults with MCI. A descriptive correlational study. A consecutive sample (N=154) of adults aged over 60years was recruited from a community healthcare centre between June and September 2016. MCI was detected using the Montreal Cognitive Assessment. Depressive symptoms and subjective memory were measured using the Geriatric Depression Scale and Memory Inventory for the Chinese, respectively. Hierarchical regression was performed to explore the relationship between SMC and depressive symptoms, with control over objective cognitive function, socio-demographic and health-related confounding factors. After controlling objective cognitive function and other confounding factors, SMC was independently associated with depressive symptoms (standardised β=0.336, p<.001). This psychological status even explained for a greater variance (R2 =8.8%) for SMC compared with objective cognitive function (R2 =2.4%). subjective memory complaint was independently associated with depressive symptoms in older adults with MCI. Early detection and management of depressive symptoms are highly important amongst this clinical cohort. Early detection and prompt treatment of depressive symptoms is a highly prioritised care agenda in managing SMC in older adults with MCI.

The Association Between Distinct Frontal Brain Volumes and Behavioral Symptoms in Mild Cognitive Impairment, Alzheimer's Disease, and Frontotemporal Dementia.

Our aim was to investigate the association between behavioral symptoms of agitation, disinhibition, irritability, elation, and aberrant motor behavior to frontal brain volumes in a cohort with various neurodegenerative diseases. A total of 121 patients with mild cognitive impairment (MCI, n = 58), Alzheimer's disease (AD, n = 45) and behavioral variant frontotemporal dementia (bvFTD, n = 18) were evaluated with a Neuropsychiatric Inventory (NPI). A T1-weighted MRI scan was acquired for each participant and quantified with a multi-atlas segmentation method. The volumetric MRI measures of the frontal lobes were associated with neuropsychiatric symptom scores with a linear model. In the regression model, we included CDR score and TMT B time as covariates to account for cognitive and executive functions. The brain volumes were corrected for age, gender and head size. The total behavioral symptom score of the five symptoms of interest was negatively associated with the volume of the subcallosal area (β = −0.32, p = 0.002). High disinhibition scores were associated with reduced volume in the gyrus rectus (β = −0.30, p = 0.002), medial frontal cortex (β = −0.30, p = 0.002), superior frontal gyrus (β = −0.28, p = 0.003), inferior frontal gyrus (β = −0.28, p = 0.005) and subcallosal area (β = −0.28, p = 0.005). Elation scores were associated with reduced volumes of the medial orbital gyrus (β = −0.30, p = 0.002) and inferior frontal gyrus (β = −0.28, p = 0.004). Aberrant motor behavior was associated with atrophy of frontal pole (β = −0.29, p = 0.005) and the subcallosal area (β = −0.39, p < 0.001). No significant associations with frontal brain volumes were found for agitation and irritability. We conclude that the subcallosal area may be common neuroanatomical area for behavioral symptoms in neurodegenerative diseases, and it appears to be independent of disease etiology.

Effect of computerised cognitive training on cognitive outcomes in mild cognitive impairment: a systematic review and meta-analysis

ObjectivesTo determine the effect of computerised cognitive training (CCT) on improving cognitive function for older adults with mild cognitive impairment (MCI).DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase, Web of Science and...

Effects on cognition of 20-day anodal transcranial direct current stimulation over the left dorsolateral prefrontal cortex in patients affected by mild cognitive impairment: a case-control study.

Mild cognitive impairment (MCI) is a common disorder affecting as much as 15% of the elderly population. Transcranial direct current stimulation (tDCS) is a non-invasive technique of neuromodulation that has proven to influence performance in different cognitive domains. We investigated the effects on cognition of 20-day anodal tDCS in 17 MCI patients compared with 17 matched MCI patients. Patients underwent neuropsychological evaluation at baseline and then were randomly assigned to the anodal or sham group. The tDCS protocol consisted in 20min, 5days per week (up to a total of 20days), of 2-mA anodal stimulation over the left dorsolateral prefrontal cortex (DLPFC). The location of anodal electrode was chosen in accordance with previous reports which relate anodal stimulation of this site with cognitive enhancement. At the end of the last day of stimulation, a second neuropsychological evaluation was performed. We compared baseline and post-stimulation neuropsychological results in the anodal vs sham group using repeated measures ANOVA as a statistical analysis test. At follow-up, patients exposed to anodal stimulation showed improvement in episodic verbal memory (p < 0.001) and figure naming test (p < 0.01), in a general index of cognitive function (Brief Mental Deterioration Battery) (p < 0.0001) and in a mood measurement test (Beck Depression Inventory) (p < 0.01). Anodal tDCS could be a useful tool to improve cognitive symptoms in MCI although more evidence is needed to understand the exact underlying mechanisms. Confirmation of its potential benefits in MCI would be significant.

Symptom Clusters of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Their Comparative Risks of Dementia: A Cohort Study of 8530 Older Persons

ObjectivesNeuropsychiatric symptoms (NPS) have been recognized to increase the risk of dementia among individuals with mild cognitive impairment (MCI). However, it is unclear whether the risk is shared across the various NPS or driven primarily by selected few symptoms. This study sought to provide confirmatory evidence on the comparative risk of dementia across the various NPS in MCI. DesignCohort study (median follow-up 4.0 years; interquartile range 2.1–6.4 years). SettingAlzheimer's Disease Centers across the United States. ParticipantsParticipants ≥60 years of age and diagnosed with MCI at baseline (n = 8530). MeasuresParticipants completed the Neuropsychiatric Inventory–Questionnaire at baseline and were followed up almost annually for incident dementia. Symptom clusters of NPS, as identified from confirmatory factor analyses, were included in Cox regression to investigate their comparative risks of dementia. ResultsThree symptom clusters of NPS were identified among participants with MCI, namely hyperactivity, affective, and psychotic symptoms. The risk of dementia was present among participants with affective symptoms [hazard ratio (HR) 1.6, 95% confidence interval (CI) 1.4–1.9] and psychotic symptoms (HR 1.6, 95% CI 1.2–2.2), but not among those with hyperactivity symptoms (HR 1.1, 95% CI 0.9–1.3). The risk was higher when affective symptoms and psychotic symptoms co-occurred (HR 2.5, 95% CI 2.0–3.2), with one-half of the participants in this group developing dementia within 2.7 years of follow-up. Conclusions and ImplicationsThe findings illustrate the potential usefulness of NPS as a convenient prognostic tool in the clinical management of MCI. They also suggest the need for future research to focus on affective/psychotic symptoms in MCI when studying the neurobiological links between NPS and neurodegenerative processes.

Reasoning with smart objects’ affordance for personalized behavior monitoring in pervasive information systems

The miniaturization of sensors and their integration in everyday appliances have opened the way for ecologically monitoring people’s behavior based on their interaction with smart objects. Thanks to behavior monitoring, mobile, and ubiquitous information systems in the areas of e-health, home automation, and smart cities are becoming more and more “smart,” being able to dynamically adapt themselves to the current users’ context and situation. However, human behavior is characterized by large variability due to individual habits, physical disabilities or cognitive impairment. This aspect makes behavior monitoring a challenging task. On the one side, execution variability makes it hard to acquire sufficiently large activity datasets needed by supervised learning methods. On the other side, being based on a strict definition of activities in terms of constituting simpler actions, existing knowledge-based frameworks fall short in adapting to the specific characteristics of the subject. Hence, the variability of activity execution by different subjects calls for personalized methods to capture human activities and interaction in smart spaces at a fine-grained level. In this paper, we address this challenge by proposing a novel hybrid reasoning framework to capture fine-grained interaction with smart objects considering the specific features of individuals. Our model has its roots in the well-founded psychological theory of affordances, i.e., those features of an object that naturally explain its possible uses and how it should be used. The core of the framework is the ontological model of smart objects affordance, expressed through the OWL 2 Web Ontology Language. Through a use case in pervasive healthcare, we show how our framework can be applied to personalized recognition of abnormal behaviors. In particular, we tackle a particularly challenging issue: how to recognize early behavioral symptoms of mild cognitive impairment in subjects with physical disabilities. Moreover, an extensive experimental evaluation with real-world datasets acquired from 24 subjects shows the effectiveness of our framework in recognizing human activities and fine-grained manipulative gestures in different pervasive computing environments.