Detection Of Cognitive Decline Research Articles

Identification of Plasma Proteins as Biomarkers for Mild Cognitive Impairment and Alzheimer's Disease Using Liquid Chromatography-Tandem Mass Spectrometry.

Blood proteins can be used for biomarkers to monitor the progression of cognitive decline, even in the early stages of disease. In this study, we developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based blood test to identify plasma proteins that can be used to detect mild cognitive impairment (MCI) and Alzheimer's disease (AD). Using this system, we quantified plasma proteins using isotope-labeled synthetic peptides. A total of 192 patients, including 63 with AD, 71 with MCI, and 58 non-demented controls (NDCs), were analyzed. Multinomial regression and receiver operating characteristic (ROC) analyses were performed to identify specific combinations of plasma protein panels that could differentiate among NDCs, those with MCI, and those with AD. We identified eight plasma protein biomarker candidates that can be used to distinguish between MCI and AD. These biomarkers were associated with coagulation pathways, innate immunity, lipid metabolism, and nutrition. The clinical potential to differentiate cognitive impairment from NDC was assessed using area under the curve values from ROC analysis, which yielded values of 0.83 for males and 0.71 for females. This LC-MS-based plasma protein panel allows the pathophysiology of AD to be followed through detection of cognitive decline and disease progression markers.

Down syndrome and Alzheimer's disease: A scoping review of functional performance and fall risk.

Alzheimer's disease (AD) occurs in aging adults with Down syndrome (DS) at a higher prevalence and an earlier age than in typical aging adults. As with the general aging adult population, there is an urgent need to understand the preclinical and early phases of AD progression in the adult population with DS. The aim of this scoping review was to synthesize the current state of the evidence and identify gaps in the literature regarding functional activity performance and falls and their significance to disease staging (i.e., mild, moderate, and severe defined staging criteria) in relation to Alzheimer's disease and related dementias (ADRD) in adults with DS. This scoping review included six electronic databases (e.g., PsycInfo, Academic Search Complete, CINAHL, COCHRANE Library, MEDLINE, and PubMed). Eligible studies included participants with DS ≥25 years of age, studies with functional measures and/or outcomes (e.g., activities of daily living, balance, gait, motor control, speech, behavior, and cognition; falls; and fall risks), and studies that investigated AD pathology and implications. Fourteen eligible studies were included and categorized through a thematic analysis into the following themes: (1) physical activity and motor coordination (PAMC), (2) cognition, (3) behavior, and (4) sleep. The studies indicated how functional activity performance and engagement may contribute to early identification of those at risk of cognitive decline and AD development and/or progression. There is a need to expand the research regarding ADRD pathology relative to functional outcomes in adults with DS. Functional measures related to disease staging and cognitive impairment are essential to understanding how AD progression is characterized within real-world settings. This scoping review identified the need for additional mixed-methods research to examine the use of assessment and intervention related to function and its detection of cognitive decline and AD progression.

An AI‐based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

AbstractBackgroundReliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide‐spread screening for dementia and accelerate preventative efforts.MethodWe used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI.ResultWe evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held‐out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub‐tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task.ConclusionThe proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develop a remote screening tool that could be easily adapted to any language.

Alteration of Visuospatial System as an Early Marker of Cognitive Decline: A Double-Center Neuroimaging Study.

Amnestic-type mild cognitive impairment (a-MCI) represents the prodromal phase of Alzheimer's disease associated with a high conversion rate to dementia and serves as a potential golden period for interventions. In our study, we analyzed the role of visuospatial (VS) functions and networks in the recognition of a-MCI. We examined 78 participants (32 patients and 46 controls) in a double-center arrangement using neuropsychology, structural, and resting-state functional MRI. We found that imaging of the lateral temporal areas showed strong discriminating power since in patients only the temporal pole (F = 5.26, p = 0.034) and superior temporal gyrus (F = 8.04, p < 0.001) showed reduced cortical thickness. We demonstrated significant differences between controls and patients in various neuropsychological results; however, analysis of cognitive subdomains revealed that the largest difference was presented in VS skills (F = 8.32, p < 0.001). Functional connectivity analysis of VS network showed that patients had weaker connectivity between the left and right frontotemporal areas, while stronger local connectivity was presented between the left frontotemporal structures (FWE corrected p < 0.05). Our results highlight the remarkable potential of examining the VS system in the early detection of cognitive decline. Since resting-state setting of functional MRI simplifies the possible automatization of data analysis, detection of VS system alterations might provide a non-invasive biomarker of a-MCI.

Single-Channel EEG Features Reveal an Association With Cognitive Decline in Seniors Performing Auditory Cognitive Assessment.

BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.MethodsThis study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.ResultsMMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24–27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.DiscussionThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.Trial RegistrationNIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].

Detection of cognitive decline by spinal posture assessment in health exams of the general older population

The recent increase in the older adult population has led to a higher prevalence of cognitive impairment, which is often overlooked in routine health examinations. Citizens aged 50–89 years were targeted for this cohort survey by random sampling from the resident registry of a cooperating town in 2014. A total of 411 participants (202 male and 209 female) were enrolled. We analyzed the distribution of cognitive function test scores as determined by Montreal Cognitive Assessment and Mini-Mental State Examination tests in each age (50’s, 60’s, 70’s and 80’s) and sex group to examine whether cognitive decline could be detected by sagittal spinal balance measurement based on a radiological approach. Sagittal spinal balance was quantitatively measured as sagittal vertical axis (SVA). We observed significant associations for higher age and/or SVA anteriorization with lower cognitive function. In males, spinal balance anteriorization was associated with cognitive decline independently of age, with combinations of age and SVA also making valid cognitive decline determinations; male cases of SVA ≥ 100 mm at any age, SVA ≥ 90 mm at ≥ 70 years, and SVA ≥ 70 mm at ≥ 80 years were all more likely to have cognitive decline than cases below those values. For females, cognitive decline was more likely in cases of SVA ≥ 70 mm, regardless of age. Thus, spinal balance anteriorization can be regarded as an easily visible indicator of latent cognitive decline in community-dwelling older people.

Evaluation of early non-cognitive markers of dementia

Population aging is inextricably linked to an increase in the prevalence of dementia, which are one of the main causes of loss of autonomy in old age. However, early detection of cognitive decline is critical to proactively implementing interventions to slow the progression of cognitive decline and maintain the ability to live independently of outside help. Therefore, early detection of cognitive decline is one of the primary tasks of the general practitioner.&#x0D; The aim of the lecture is to present a low gait speed, frailty, neuropsychiatric symptoms, sleep disorders and hearing loss as biomarkers of high risk to development of dementia risk and suggest ways measuring these risk factors in primary health care.

Digital speech‐based measures separate those with cognitive impairment from those without

AbstractBackgroundMeasures of language have demonstrated value in the detection of cognitive decline. Here we automatically extracted speech (audio) and language (transcripts) features from Cookie Theft picture descriptions (BDAE) to develop two classification models separating healthy participants from those with mild cognitive impairment (MCI) and dementia.MethodWe used Dementia Bank and Wisconsin Registry for Alzheimer’s Prevention (WRAP) datasets and evaluated the first observation of Cookie Theft descriptions from 1011 healthy participants: 51 with MCI (based on MMSE scores) and 193 with dementia. From the samples, we automatically extracted metrics to fit two classification models, which: (1) separated healthy from MCI participants; and (2) separated healthy from dementia participants. To avoid overfitting and spurious patterns in the data, we used k‐fold cross‐validation, restricted the feature space to only clinically meaningful metrics, and used simple logistic regression models with no more than 5 predictors.ResultThe MCI classifier separated healthy and MCI groups (ROC AUC = .80; Figure 1). Predictors were: MATTR (proportion of unique words on 10‐word moving average; b = ‐16.0, z = ‐4.7, p‐val &lt; .05); the ratio of pronouns to nouns (b = 28.1, p‐value &lt; .05), and propositional density (b = ‐15.0, p‐value &lt; .05). The dementia classifier separated healthy and dementia groups (ROC AUC = .89; Figure 2). The predictors were: parse tree height (sentence complexity; b = ‐1.3, p‐value &lt; .05), mean length of word (surface form; b = ‐3.9, p‐val &lt; .05), the proportion of relevant details correctly identified in a picture description (b = 10.7; p &lt; .05), type‐to‐token ratio (proportion of unique words; b = ‐11.0; p‐value &lt; .05), and the duration of pauses relative to the total speaking duration (b = 1.1, p‐value = .22).ConclusionBoth classifiers separated groups with ROC AUC &gt;= 0.80, with the dementia classifier performing better than MCI classifier. Features in the models were clinically meaningful and interpretable, and relate to vocabulary (MATTR, propositional density, TTR, mean word length), syntax (parse tree height), language processing (pause duration/speaking duration), and ability to convey relevant picture details.

Normative Data for the CERAD-NP for Healthy High-Agers (80-84 years) and Effects of Age-Typical Visual Impairment and Hearing Loss.

This study aims to establish reference data for nondemented adults between 80 and 84 years of age based on the German version of the Consortium to Establish a Registry for Alzheimer's disease Neuropsychological (CERAD-NP) test battery and to assess the possible influence of hearing and vision impairments on CERAD-NP performance. Two hundred one volunteers were examined with the German CERAD-NP test battery, and 18 test scores were calculated from the data. The sample included 99 men (49%), the mean age was 81.8 years (SD = 1.3), and the mean years of education were 13.9 (SD = 3.1). Percentiles for continuous and percentile ranks for discrete test scores were calculated separately for four norm groups. The groups were classified according to gender and education. Multiple regression analysis was used to predict cognitive performance from visual acuity and hearing ability. The normative data obtained were consistent with other findings from younger and older age groups. Worse visual acuity predicted slower performance in the Trail Making Test (TMT). None of the other CERAD-NP tests were correlated to sensory functions. Using age-appropriate reference data, such as that established here for the 80-84 year age group can help to improve the detection of cognitive decline and prevent biases that arise when old-old adults are compared to younger old adults. Visual acuity should be considered an influencing factor on TMT performance.

Longitudinal Cognitive Decline in Patients With Mild Cognitive Impairment or Dementia Due to Alzheimer\u2019s Disease

Sensitive cognitive assessments accurately detect and track cognitive decline in Alzheimer’s disease. The Cogstate battery was used to measure cognitive change in cognitively normal participants and in individuals with mild cognitive impairment and mild Alzheimer’s disease enrolled in the Australian Imaging, Biomarker and Lifestyle Rate of Change Substudy. Over 18 months, verbal episodic memory performance declined for mild cognitive impairment and mild Alzeheimer’s disease groups when compared to cognitively normal participants. Frequent assessments of episodic memory may facilitate early detection of cognitive decline due to Alzheimer’s disease.

Improving Detection of Cognitive Decline and Impairment Using Everyday Behavioral and Social Markers

Early detection of cognitive decline and mild cognitive impairment (MCI) during the pre-symptomatic phase of Alzheimer’s disease is particularly important for maximizing effectiveness of clinical trials and efficiency of resource allocation. However, it is difficult to distinguish early signs of decline and impairment from normative aging, especially with biomarkers and clinical-based assessments that are expensive and challenging to apply widely. Ambulatory assessments in naturalistic settings provide opportunities to capture everyday markers of cognitive decline and offer cost-effective tools for sensitive, early detection of transitions to MCI in community-dwelling older adults. In this symposium, we present four studies that use ecological momentary assessment (EMA) data from the Einstein Aging Study to showcase how everyday markers of behavioral and social functioning assessed up to six times a day for 14 consecutive days can facilitate early detection of cognitive difficulties. Zhaoyang et al. examine whether older adults with intact cognition versus MCI differ in patterns of daily social interactions. Hyun et al. investigate how the diversity of daily activities is associated with ambulatory cognitive deficits. Cerino et al. compare the sensitivity of everyday markers of stress versus global trait-based stress measures to detect MCI. Roque et al. use completion time from EMA surveys as a reliable and unobtrusive way to measure cognition and distinguish those with and without MCI. Stacey Scott will integrate insights gained from these four papers, and discuss the opportunities and challenges faced when combining ambulatory assessments of everyday markers with traditional methods to better detect cognitive decline and impairment.

New Perspectives on Predicting and Modifying Cognition in Older Adulthood

The underlying theme of my research is to advance our understanding of cognitive aging through early detection of cognitive decline and the promotion of positive lifestyle factors. I will present my past and recent work that uses new perspectives to predict and modify cognition with older age, specifically focusing on intraindividual variability in cognitive speed, and lifestyle activity engagement. This includes examining the utility of variability in cognitive speed, usually a predictor of decline, instead as a predictor of cognitive plasticity following activity interventions. Further, the association between activity engagement and later cognitive ability and dementia risk is more complex than first appears. I will present research that looks at critical issues about the activity-cognition relation from a different perspective, including unique frameworks investigating the ideal way to measure activity, and the duration of how long past activity can potentially impact cognition. Finally, I will discuss my future research directions.

Early detection of cognitive impairment by natural language among outpatients and community dwellers

Background: Although early detection of cognitive decline has a significant relation to improving the quality of life of dementia patients, this early detection has been difficult due to requires of neuropsychological tests which people generally take when they notice their cognitive impairment. The timing of patients’ notice was reported to be worse cognitive decline already, thus, we aimed to determine if cognitive impairment from a short interview by using Natural Language Processing approach. Methods: The present study used cross-sectional analysis among elderly outpatients and community-dwelling elderly from Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians (SONIC) study. Cognitive decline was assessed by Telephone Interview of Cognitive Status for Japanese (TICS-J) and modeled as a binary outcome (cut-off <33 points). Natural language data was collected by semistructured interviews about health conditions and cognitive orientation in space, time, and place. We used an open-source text segmentation library to parse natural language text into bag-of-words and term frequency-inverse document frequency (TF-IDF) representations. Results: There were 38 (19.9%) outpatients and 153 (80.1%) community dwellers, and 60 (31.4%) participants were defined as cognitive impairment. The maximized TF-IDF score was 0.49 in cognitive orientation in time questions. In this question, participants without cognitive impairment could not calculate the score. There were no significant differences in TF-IDF scores between participants with and without cognitive impairment. Conclusions: Elderly without cognitive impairment might not have an episode about cognitive orientation in time, and this may help for early detection of cognitive impairment

Automatic early detection of cognitive decline

AbstractBackgroundWhile it is agreed that cognitive decline is underdiagnosed, there is still no single universally accepted and affordable screening method that satisfies all needs in the detection of cognitive impairment. The goal of this presentation is to demonstrate an automatic cognitive decline assessment with Neurosteer Aurora, utilizing a novel EEG analysis as a method for characterization of cognitive activity and detection of early cognitive impairment.MethodNeurosteer has developed a miniature wearable neurological sensor combined with an automatic assessment tool for a wide range of brain monitoring and treatment needs. It is based on a single EEG channel and relies on advanced mathematical analysis for a decomposition of the EEG signal into multiple components. These components are then processed with machine learning algorithms to create high level biomarkers for real‐time brain activity interpretation. Additionally, Neurosteer has developed a fully automatic assessment and biomarkers for cognitive decline. It is based on a verbal and musical cognitive assessment during brain activity recording. Machine learning algorithms analyze the combined data to produce a simple‐to‐read report indicating the level of different aspects of cognitive decline. We applied this technology on three different healthy population (N=14, 40, 215) and cognitively impaired (N=24, 10, 9, 8) undergoing several automatically administered cognitive tasks.ResultA few biomarkers were extracted using machine learning tools on the collected data. The biomarkers distinguished between different levels of cognitive load as well as resting state patterns. The new groups of cognitively impaired populations were examined with Neurosteer Aurora, completing short and automatic auditory assessments. The patterns of the predefined biomarkers were significantly different for each population, differentiating between clinical populations and negatively correlated with the severity of cognitive decline.ConclusionUsing the novel brain activity interpretation and biomarkers enables an automatic and easy‐to‐administer cognitive decline assessment in the clinical population. Such assessment is a step forward towards a wide‐spread screening, allowing early and personalized intervention to slow down cognitive decline, improving health and quality of life outcomes.

Detection of cognitive decline in metabolic syndrome

Abstract Background Metabolic syndrome (MetS), a risk factor for many vascular conditions, may be a prodromal manifestation of vascular cognitive impairment. Diagnosing early stages of cerebrovascular pathology can lead to prevention and delay of the progression of pathological conditions such as vascular cognitive impairment. Methods The objective of the study was to investigate new biomarkers for early diagnosis of MetS and cognitive decline as a follow-up. A cardiological, neuropsychological and neurological study was conducted among 75 Bulgarian participants. Beta amyloid in the blood, procalcitonin (PCT), NT-proBNP as predictors of cognitive impairment in patients with metabolic syndrome were identified. Clinical, anthropometric, biochemical, neuropsychological, cognitive and statistical data processing. Plasma amyloid beta (Aβ) levels, procalcitonin, NT-proBNP in MetS were investigated in participants with MetS and in group of healthy people. Results In the present study, plasma levels of Aβ42 and Aβ40 were found to be reduced in MetS participants. Procalcitonin concentration was significantly higher in males than in females. NT-proBNP was significantly higher in females than in males (p &amp;lt; 0.001). Regression analysis showed a positive relationship between NT-proBNP and systolic blood pressure (p &amp;lt; 0.001) and fasting blood glucose (p &amp;lt; 0.05). An inverse relation between NT-proBNP and diastolic blood pressure, waist circumference, triglycerides, HDL- and LDL cholesterol was found. Conclusions There was a positive association between PCT levels, decreased levels of Aβ42 and Aβ40, as well as elevated NT-proBNP and cognitive impairment in people with MetC. A concentration of NT-proBNP of 60 pg / ml or greater could be an indicator of metabolic abnormalities and early cognitive decline. Key messages Metabolic syndrome may be a prodromal manifestation of vascular cognitive impairment. The use of new biomarkers for diagnosis can lead to preventing and slowing the progression of complications associated with MetS.

The NIH Toolbox Cognition Battery App in healthy aging and cognitive decline due to Alzheimer’s Disease and Mild Cognitive Impairment

Abstract Introduction There has been a significant increase in average life expectancy. This increase brought more focus on aging with more health, autonomy and independence. Among current public health concerns, the detection of cognitive decline in older individuals stands out, namely in Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI). Accurate, brief, practical and automated measures are needed to assess cognitive function throughout the life-span. Since 2015, there is the NIH Toolbox Cognition Battery (NIHTB-CB) app that meets these requirements and assesses the neurocognitive subdomains of attention, episodic memory, executive function, language, processing speed, and working memory. The European Portuguese app, developed by our team, will be validated for the Portuguese older adults. Objectives The aim is presenting a literature review of the use of NIHTB-CB in healthy aging and cognitive decline in MCI and AD. Methodology Advanced search in the databases of Web of Science and Google Scholar for studies published between 2016-2019, including articles and meeting abstracts with the words: NIH Toolbox Cognition Battery, AD, MCI, Elder or Senior or Older. Results According to the studies reviewed, NIHTB-CB may be useful in memory clinics (e.g.Hackett et al, 2018; Mayeda et al. 2018); clinical trials (e.g.Buckley et al., 2017; Parsey, Bagger &amp; Hanson, 2019); and healthy aging (e.g.Scott, Sorell, &amp; Benitez, 2019). Preliminary results of the ARMADA study (Weintraub et al, 2019) with people with more than 85 years old became available showing that generally, NIHTB-CB is well accepted, also in MCI patients. No difficulties were found in the use of the iPad with older adults. Conclusion NIHTB-CB measures provide a valid assessment of neurocognitive domains that are important in healthy aging, MCI and AD. As the studies are still scarce, more research is needed.

Sensor-based systems for early detection of dementia (SENDA): a study protocol for a prospective cohort sequential study

BackgroundDementia and cognitive decline are serious social and economic burdens. An increase in the population of older people, as well as longer lifespans mean that numbers of dementia cases are exponentially rising. Neuropathological changes associated with dementia are thought to appear before the clinical manifestation of cognitive symptoms, i.e., memory impairments. Further, some older adults (OA) experience cognitive decline before it can be objectively diagnosed. For optimal care of these patients, it is necessary to detect cognitive decline and dementia at an early stage. In this vein, motor, sensory, and neurophysiological declines could be promising factors if found to be present before the onset of cognitive impairment. Hence, the objective of the SENDA study is to develop a multi-dimensional sensor-based instrument that allows early detection of cognitive decline or dementia in OA with the help of cognitive, sensory, motor, and neurophysiological parameters before its clinical manifestation.Methods/designIn the cohort sequential study, participants are assigned to one of three study groups depending on their cognitive status: 1. cognitively healthy individuals (CHI), 2. subjectively cognitively impaired persons (SCI), or 3. (possible) mildly cognitively impaired persons (pMCI, MCI). All groups take part in the same cognitive (e.g., executive function tests), motor (e.g., gait analyses, balance tests), sensory (e.g., vibration perception threshold test, proprioception tests), and neurophysiological (e.g., electroencephalograms) measurements. Depending on the time at which participants are included into the study, all measurements are repeated up to four times in intervals of 8 months within 3 years to identify associations with cognitive changes over time.DiscussionThis study aims to detect possible motor, sensory, neurophysiological, and cognitive predictors to develop an early screening tool for dementia and its pre-stages in OA. Thus, affected persons could receive optimal health care at an earlier time point to maintain their health resources.Trial statusThe study is ongoing. The recruitment of participants will be continued until May 2020.

Computerized Cognitive Tests (CCT) in elderly: A psychometric review

Introduction/aimComputerized Cognitive Tests (CCT) play an increasing role in the assessment of elderly cognitive impairment. In this framework, it is important to review the psychometric data of the available CCT in the detection of cognitive decline. MethodTo analyse psychometric properties of the CCT, the available data concerning reliability and validity indices were considered. ResultsOnly eleven CCT identified with elderly were retained to systematic review. 9 of 11 CCT present some results about their reliability properties; 8 of 11 CCT present results from concurrent validity; only 4 of 11 CCT present data concerning criterion validity; and just 4 of 11 CCT present some results regarding factor analysis. ConclusionThere are only a few papers published with well-structured psychometric data (reliability and validity). Some results have important limitations concerning to the adequacy of reliability and validity indices and some of these CCT psychometric properties were not studied yet. Considering these limitations, more research on the CCT is needed, including systematic studies of their psychometric properties, and Item Response Theory should be considered.

Sensitivity and specificity of a briefer version of the Cambridge Cognitive Examination (CAMCog-Short) in the detection of cognitive decline in the elderly: An exploratory study.

To create a reduced and briefer version of the widely used Cambridge Cognitive Examination (CAMCog) battery as a concise cognitive test to be used in primary and secondary levels of health care to detect cognitive decline. Our aim was to reduce the administration time of the original test while maintaining its diagnostic accuracy. On the basis of the analysis of 835 CAMCog tests performed by 429 subjects (107 controls, 192 mild cognitive impairment [MCI], and 130 dementia patients), we extracted items that most contributed to intergroup differentiation, according to 2 educational levels (≤8 and >8y of formal schooling). The final 33-item "low education" and 24-item"high education" CAMCog-Short correspond to 48.5% and 35% of the original version and yielded similar rates of accuracy: area under ROC curves (AUC)>0.9 in the differentiation between controls×dementia and MCI×dementia (sensitivities>75%; specificities>90%); AUC>0.7 for the differentiation between controls and MCI (sensitivities>65%; specificities>75%). The CAMCog-Short emerges as a promising tool for a brief, yet sufficiently accurate, screening tool for use in clinical settings. Further prospective studies designed to validate its diagnostic accuracy are needed.

Steady state visual evoked potential based brain-computer interface for cognitive assessment.

Cognitive assessment is of growing importance, with the general population getting older and a rapidly growing incidence of dementia, which is a major public health issue. Treatment of dementia must, to be most effective, start early in the disease process. Thus, early detection of cognitive decline is important. Cognitive decline may be detected using fully-automated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% - 0%. The test subjects achieved an information transfer rate (ITR) of 14.64 bits/min - 7.63 bits/min and a subject test performance of 47.22% - 34.10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system to be fully valid and of clinical use.