Early Signs Of Cognitive Decline Research Articles

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Using 6-CIT, P300 encephalography, and pro-inflammation assessments for screening age-related cognitive decline and exploring associated risk factors in Egyptian elderly

BackgroundThe elderly population is suffering from many mental health problems that are aggravated as a matter of age and cognitive decline is a serious one of them. The aim of the present work is to screen the cognitive performance among a sample of Egyptian elderly volunteers and to investigate the associated risk factors.ResultsA sample of 88 elderly volunteers from both genders was enrolled in the study according to the specified eligibility criteria after signing the approval consent. Medical history and socio-demographic data were collected from all participants in addition to basic clinical examination. Cognitive performance was assessed using the 6-Item Cognitive Impairment Test (6-CIT) while the endogenous event-related potentials (ERP) was measured using P300. The inflammatory biomarkers; TNF-α and COX-2 levels were assessed in serum using ELISA technique in addition to gene expression of TNF-α, PPAR-γ and CD-36 exploration using qRT-PCR. About half (51%) of the sample under investigation showed cognitive problems with scores on the 6-CIT exceeding the normal level. TNF-α serum levels showed positive correlation with P300 latency and correlated negatively with P300 reaction time. Furthermore, serum COX-2 levels correlated positively with P300 reaction time and negatively with P300 amplitude.ConclusionThe study population is showing early signs of cognitive decline that invites attention to the importance of spreading preventive measures against further deterioration. Inflammatory biomarkers under investigation and 6-CIT are suggested to be used in prediction of early stages of cognitive decline among the elderly population.

Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study.

A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.

Brief Cognitive Screener Detects Early Signs of Cognitive Decline in Anglo and Hispanic Patients.

Brief Cognitive Screener Detects Early Signs of Cognitive Decline in Anglo and Hispanic Patients.

The Digital Clock Drawing Test and Delayed Recall Predict Informant‐Rated Functional Impairment in Individuals with Cognitive Impairment

AbstractBackgroundNearly 6.7 million people aged 65 and older in the US are living with Alzheimer’s disease (AD). Early detection of dementia presents a difficult challenge, as the earliest signs of cognitive decline often go undetected. Thus, more sensitive and clinically meaningful cognitive screening tools are needed. Evaluating the patient’s dependence in activities of daily living is critical to detect progression toward dementia. The Linus Health DCTclock™ employs AI‐enabled analyses of the clock drawing process to evaluate cognitive and motor function. We evaluated the utility of four DCTclock composite scales including Spatial Reasoning, Drawing Efficiency, Information Processing, and Motor Skills, 22 DCTclock subscales, and the 3‐word Delayed Recall score from the Digital Clock and Recall (DCR™) to predict the informant‐rated functional activities questionnaire (FAQ) score in older adults ranging from cognitively unimpaired to individuals with mild cognitive impairment (MCI) and AD‐related dementia (ADRD).Method939 participants in a multisite study (age mean±SD = 72.1±6.7; 57.1% female; years of education mean±SD = 15.4±2.7; primary language English), classified as cognitively unimpaired (n = 406), MCI (n = 293), or ADRD (n = 238) were included. We conducted cross‐validated regularized logistic regressions with FAQ score ≥6 or ≥9 (mild or moderate functional impairment, respectively) as dependent variable and DCTclock composite scales, subscales, and Delayed Recall score as predictors, in addition to age, sex, and education.ResultCombination of DCTclock subscales and Delayed Recall predicted FAQ≥6 and ≥9 with AUCs = 0.79 and 0.81, respectively. DCTclock composite scales predicted FAQ≥ 6 and ≥9 with AUCs = 0.75 and 0.77, respectively. Combination of DCTclock composite scales and Delayed Recall predicted FAQ≥6 and ≥9 with AUCs = 0.77 and 0.79, respectively. DCTclock subscales by themselves predicted FAQ≥6 and ≥9 with AUCs = 0.77 and 0.79, respectively.ConclusionThe DCR, a brief digital cognitive assessment with automatic scoring that can be completed in primary‐care settings, can infer the effect of cognitive impairment on a patient’s functional activity with great accuracy and within a few minutes. This makes the DCR an empowering tool for primary‐care providers in practical evaluation of patients with cognitive impairment.

Longitudinal relationships between grip strength, subjective memory complaints and cognitive function among middle-aged and older adults in China.

Subjective memory complaints are considered an early sign of cognitive decline. Recent evidence shows that grip strength is an important predictor of cognitive function. However, few studies have compared whether one condition is uniquely associated with cognitive function when another condition is controlled for. To explore the longitudinal associations of cognitive function with subjective memory complaints and grip strength in middle-aged and older adults, with a particular focus on whether one condition is uniquely associated with cognitive function when another condition is considered. A total of 3,877 middle-aged and older adults (aged 45-92years) from the China Health and Retirement Longitudinal Study reported on their demographic and health status and completed measures of grip strength and subjective memory complaints, as well as a series of cognitive tests, every two years between 2011 and 2015. Generalized estimating equation models were used to assess the relationships between grip strength, subjective memory complaints, and cognitive function. Grip strength was longitudinally associated with cognitive function (β = 0.021, 95% confidence interval [CI]: 0.011, 0.030, P < 0.001) independent of subjective memory complaints. In contrast, changes in the subjective memory complaints were not related to cognitive function over time (β = 0.107, 95% CI: - 0.025, 0.238, P = 0.112). Only at baseline subjective memory complaints were associated with poorer cognitive function (β = - 1.142, 95% CI: - 1.309, - 0.975, P < 0.001). Grip strength might be a more important clinical correlate of cognitive function decline over time than subjective memory complaints. Regular assessment and close monitoring of grip strength might help identify individuals who might be at high risk for cognitive impairment.

A cross-platform application for the ecological and remote assessment of memory impairment in aging: ECO-MEMORY

AbstractThis work aims to present the first step of a creation of an instrument to assess memory deficits responding to the needs imposed by the inability to access clinical care, such as physical or geographical constraints or still limitations imposed during the pandemic era. The older population, who would benefit from these services, may be at risk as access to services that support psychological and neuropsychological needs, which are not considered essential, has frequently been restricted in recent years. Moreover, because deficits are commonly mistaken for the effects of physiological aging, the early signs of cognitive decline might be ignored. On these bases, we used the potential of 360-degree media to create an application for memory assessment without the physical presence of clinicians: ECO-MEMORY. Firstly, we developed the application and evaluated its usability. ECO-MEMORY is divided into four sections, each addressing a different memory task: recognizing objects and faces, learning a path, and creating an allocentric map. Thirteen older adults who used the tablet application provided usability data as well as qualitative feedback on their experience. After the performance, the System Usability Scale, the Senior Technology Acceptance Model, and the Independent Television Commission Sense of Presence were administered. We performed a qualitative analysis and descriptive statistics, which showed that ECO-MEMORY is a usable instrument. Also, it was enjoyable for users who generally accepted technology in their life. ECO-MEMORY may therefore offer a promising approach to memory evaluation by including real-world scenarios.

Longitudinal Relationship Between Handgrip Strength and Cognitive Function in a European Multicentric Population Older Than 50Years.

The aim of this study was to analyze the bidirectional association between handgrip strength (HGS) and cognitive performance in different cognitive functions in a European population and to evaluate the predictive validity of HGS for the risk of future cognitive impairment in aging individuals. This was a prospective cohort study conducted using data on individuals over 50years of age from the Survey of Health, Aging and Retirement in Europe (SHARE). HGS measures and scores in numeracy, recall, and verbal fluency were repeated and analyzed biannually for 4years and were used in generalized estimating equations to test the bidirectional association, categorized by sex. Of the 8236 individuals included, 55.73% were women with a mean age of 67.55 (standard deviation [SD] = 8.4) years and 44.27% were men with a mean age of 68.42 (SD = 7.7) years. HGS predicted cognitive decline in both sexes, except for numeracy in men, even after adjustments. The strongest association with HGS in women was in verbal fluency (β = .094; 95% CI = 0.039 to 0.151), whereas the strongest association with HGS in men was in delayed verbal recall (β = .095; 95% CI = 0.039 to 0.151). Conversely, the greatest cognitive predictor of HGS decline was verbal fluency in men (β = .796; 95% CI = 0.464 to 1.128), and in women (β = .801; 95% CI= 0.567 to 1.109). There is a significant and bidirectional association between HGS and different cognitive functions in a European multicentric population. This bidirectional association differed between sexes. Both men and women who presented with cognitive decline also showed early changes in their HGS measures, and vice versa, but there still were differences between the sexes. These findings reinforce that HGS may be a simple and inexpensive method to identify early signs of cognitive decline, and that studies and rehabilitation strategies should be more sex specific.

Alzheimer’s Disease from the Amyloidogenic Theory to the Puzzling Crossroads between Vascular, Metabolic and Energetic Maladaptive Plasticity

Alzheimer's disease (AD) is a progressive and degenerative disease producing the most common type of dementia worldwide. The main pathogenetic hypothesis in recent decades has been the well-known amyloidogenic hypothesis based on the involvement of two proteins in AD pathogenesis: amyloid β (Aβ) and tau. Amyloid deposition reported in all AD patients is nowadays considered an independent risk factor for cognitive decline. Vascular damage and blood-brain barrier (BBB) failure in AD is considered a pivotal mechanism for brain injury, with increased deposition of both immunoglobulins and fibrin. Furthermore, BBB dysfunction could be an early sign of cognitive decline and the early stages of clinical AD. Vascular damage generates hypoperfusion and relative hypoxia in areas with high energy demand. Long-term hypoxia and the accumulation within the brain parenchyma of neurotoxic molecules could be seeds of a self-sustaining pathological progression. Cellular dysfunction comprises all the elements of the neurovascular unit (NVU) and neuronal loss, which could be the result of energy failure and mitochondrial impairment. Brain glucose metabolism is compromised, showing a specific region distribution. This energy deficit worsens throughout aging. Mild cognitive impairment has been reported to be associated with a glucose deficit in the entorhinal cortex and in the parietal lobes. The current aim is to understand the complex interactions between amyloid β (Aβ) and tau and elements of the BBB and NVU in the brain. This new approach aimed at the study of metabolic mechanisms and energy insufficiency due to mitochondrial impairment would allow us to define therapies aimed at predicting and slowing down the progression of AD.

Cognitive Function Trajectories and Factors among Chinese Older Adults with Subjective Memory Decline: CHARLS Longitudinal Study Results (2011-2018).

Older adults with subjective cognitive decline are at increased risk of future pathological cognitive decline and dementia. Subjective memory decline is an early sign of cognitive decline; preventing or slowing cognitive decline in at-risk populations remains an elusive issue. This study aimed to examine the cognitive trajectories and factors in older adults with subjective memory decline. Latent growth curve models (LGCMs) were fitted to examine the cognitive function trajectories and factors among 1465 older adults (aged 60+ years) with subjective memory decline. Data were obtained from four waves from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018), which is a large nationally representative sample of the Chinese population. The results showed that older adults with better initial cognition had a slower decline rate, which may be accelerated by advanced age, low-level education, a rapid decrease in instrumental activities of daily living (IADL) ability, and rapid increase in depression levels. This study was the first to examine the trajectories of cognitive function and its factors in a high-risk population with subjective memory decline. These findings may guide prevention approaches to tackle the issues of cognitive function decline and dementia.

Enhanced Machine Learning Classification of Cognitive Impairment with Multimodal Digital Biomarkers

AbstractBackgroundLinus Health’s DCTclock™ is an FDA‐listed cognitive assessment solution capable of detecting early signs of cognitive decline. DCTclock digitizes the long‐standing pen‐and‐paper Clock Drawing Test (CDT) and captures a wide variety of cognitive and motor metrics during the entire drawing process. Multiple studies have validated the DCTclock’s ability to detect early cognitive impairment and improve on existing assessment methods (e.g., Souillard‐Mandar et al. 2015 &amp; 2021, Rentz et al. 2021). Linus subsequently developed the Digital Clock and Recall (DCR™) assessment, which incorporates voice and delayed verbal recall alongside the DCTclock. The analysis below evaluated the added benefit of the DCR and the relationship between voice analytics and diagnosis.MethodThe DCR is being administered as part of the BioHermes‐Gap study. Data from 495 participants were used to correlate DCR or DCT with MMSE. A repeated‐measures ANOVA model was used to analyze speech and determine within‐subject differences between sessions (immediate repeat and delayed recall) on 40+ acoustic features extracted from 3‐word recall tasks of 664 older adults. Results were used to build Multilayer Perceptron Neural Networks (ANN) to assess the accuracy of combined acoustic features to classify cognitive status.ResultDCR is more highly correlated with MMSE than the DCTclock (r=0.43 vs. 0.38). ANN results indicate that combining acoustic features with cognitive assessments achieves greater classification accuracy for healthy (AUC=0.95), MCI (0.93), and AD (0.97) groups than models including either acoustic features or cognitive assessments alone.ConclusionA multimodal machine learning investigation of cognitive state, DCR, which includes DCTclock and voice/speech features from delayed recall, increases the accuracy (sensitivity and specificity) of cognitive status classification.

LANGaware: Robust Multimodal Machine Learning Methods for the Early Detection of Neurodegenerative and Psychiatric Diseases

AbstractBackgroundMultiple neurodegenerative and psychiatric diseases can affect speech, manifesting as subtle changes in spoken language. We perform a rigorous evaluation of a Machine Learning (ML) pipeline on a large‐scale multiaxial experimental setting, to investigate its capacity for detecting early signs of cognitive decline as manifested in speech. Using a diverse dataset of speech samples spanning different languages, patient cohorts and cognitive status diagnoses, we propose that the developed pipeline showcases robust performance, facilitating efficient decision support for early cognitive decline detection.MethodWe build upon previous research efforts on data‐driven cognitive decline prediction. Random forest models are constructed from a dataset of more than 3000 audio recordings and ASR‐generated transcripts, constituting cognitive assessment test tasks (e.g. picture description, everyday activity recounting, etc.). A multimodal NLP and audio analysis procedure derives sophisticated predictive biomarkers, using three methods for feature selection, feature decorrelation preprocessing and rigorous, grid search‐based hyperparameter optimization. We introduce novel experimental axes of investigation, including the spoken language (English and Greek), single and multi‐task verbal task settings, and different neurodegenerative diseases for the pathological cohorts (Alzheimer’s Disease or Dementia, along with age and education‐matched healthy cohorts). Four different NLP analysis tools for tokenization, syntax and dependency extraction are considered, using both ML and Deep Learning (DL) techniques. Model stability and generalization is supported via 5‐fold cross‐validation and the use of a separate test set.ResultResults are validated against medical expert diagnosis. Αverage performance across all investigative axes reaches high scores for accuracy (0.73), sensitivity (0.72), specificity (0.74), precision (0.77), F1 (0.74), and AUC (0.78). Additional findings suggest that single‐task models outperform multitask ones (e.g. reaching AUC scores of 0.85). Moreover, cognitive decline detection is more efficient for pathological cohorts of Alzheimer's, versus patients with Dementias.ConclusionThe broad empirical evaluation verifies the effectiveness of the deployed ML system, adding evidence in favor of multimodal AI solutions for predicting early cognitive decline and showcasing their robustness in diverse evaluation settings. Future work includes leveraging DL methods for classification and evaluation on differential diagnosis discrimination tasks.

Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank

BackgroundGrip strength is a widely used and well-validated measure of overall health that is increasingly understood to index risk for psychiatric illness and neurodegeneration in older adults. However, existing work has not examined how grip strength relates to a comprehensive set of mental health outcomes, which can detect early signs of cognitive decline. Furthermore, whether brain structure mediates associations between grip strength and cognition remains unknown.MethodsBased on cross-sectional and longitudinal data from over 40,000 participants in the UK Biobank, this study investigated the behavioral and neural correlates of handgrip strength using a linear mixed effect model and mediation analysis.ResultsIn cross-sectional analysis, we found that greater grip strength was associated with better cognitive functioning, higher life satisfaction, greater subjective well-being, and reduced depression and anxiety symptoms while controlling for numerous demographic, anthropometric, and socioeconomic confounders. Further, grip strength of females showed stronger associations with most behavioral outcomes than males. In longitudinal analysis, baseline grip strength was related to cognitive performance at ~9 years follow-up, while the reverse effect was much weaker. Further, baseline neuroticism, health, and financial satisfaction were longitudinally associated with subsequent grip strength. The results revealed widespread associations between stronger grip strength and increased grey matter volume, especially in subcortical regions and temporal cortices. Moreover, grey matter volume of these regions also correlated with better mental health and considerably mediated their relationship with grip strength.ConclusionsOverall, using the largest population-scale neuroimaging dataset currently available, our findings provide the most well-powered characterization of interplay between grip strength, mental health, and brain structure, which may facilitate the discovery of possible interventions to mitigate cognitive decline during aging.

Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients With Type 2 Diabetes Mellitus.

Introduction:The earliest signs of cognitive decline include deficits in temporal (time-based) speech characteristics. Type 2 diabetes mellitus (T2DM) patients are more prone to mild cognitive impairment (MCI). The aim of this study was to compare the temporal speech characteristics of elderly (above 50 y) T2DM patients with age-matched nondiabetic subjects.Materials and Methods:A total of 160 individuals were screened, 100 of whom were eligible (T2DM: n=51; nondiabetic: n=49). Participants were classified either as having healthy cognition (HC) or showing signs of MCI. Speech recordings were collected through a phone call. Based on automatic speech recognition, 15 temporal parameters were calculated.Results:The HC with T2DM group showed significantly shorter utterance length, higher duration rate of silent pause and total pause, and higher average duration of silent pause and total pause compared with the HC without T2DM group. Regarding the MCI participants, parameters were similar between the T2DM and the nondiabetic subgroups.Conclusions:Temporal speech characteristics of T2DM patients showed early signs of altered cognitive functioning, whereas neuropsychological tests did not detect deterioration. This method is useful for identifying the T2DM patients most at risk for manifest MCI, and could serve as a remote cognitive screening tool.

Self-rated health as a predictor of cognition among middle-aged and older Latinos

ABSTRACT Self-rated health (SRH) has been used to predict cognitive decline in various populations; however, this relationship has not been examined in Latinos. This study examines the relationship between SRH and cognition among middle-aged and older Latinos. A cross-sectional analysis was conducted among Latinos (n = 425, Mage = 64.13 ± 7.65, 82% female). Participants rated their health as poor/fair, good, and excellent and completed cognitive performance tests. Analyses of covariance (ANCOVA) examined differences in cognition between SRH categories. ANCOVAs showed significant differences in SRH categories for working memory (F [2, 357] = 3.63, p = .028) and global cognition (F [2, 348] = 3.074, p = .047), such that those who self-rated their health as good had better scores compared to participants in the poor/fair category. Findings show that SRH is associated with cognition among middle-aged and older Latinos. SRH may serve as an indicator of early signs of cognitive decline.

Cortical Gray to White Matter Signal Intensity Ratio as an Early Sign of Cognitive Decline in Alzheimer's Dementia and Mild Cognitive Impairment

Cortical Gray to White Matter Signal Intensity Ratio as an Early Sign of Cognitive Decline in Alzheimer's Dementia and Mild Cognitive Impairment

LANGaware: Leveraging machine learning on natural language for the early detection of neurodegenerative and psychiatric diseases

AbstractBackgroundVarious neurodegenerative and psychiatric diseases are related with changes in spoken language [1], although they have seldom been investigated. We evaluate the effectiveness of our language‐agnostic Machine Learning (ML) system to detect subtle changes in spoken language that manifest early signs of cognitive decline, thus assisting with its diagnosis. We evaluate our methodology using recordings of speech samples from multiple languages obtained from patient cohorts in early stages of cognitive decline and matched healthy controls.MethodOur methodology involves capturing patient audio recordings while they are performing one or more predefined cognitive assessment tasks, involving, e.g., the description of a picture or recounting of an everyday activity. Afterwards, multi‐language audio recordings and generated transcripts are analyzed with audio and NLP feature extraction methods [3], ranging from semantic, morpho‐syntactic, phonological representations of the input, as well as, more sophisticated linguistic measures. The feature pool is filtered by a Pearson's rho threshold of 0.85. We build a Random Forest classifier out of 100 Decision trees, using the Gini impurity criterion, 5‐fold cross‐validation for training, elimination and composition‐based feature selection, as well as post‐selection retraining / fine‐tuning. The model's diagnostic performance is evaluated on a test set unseen during training.ResultOur results are validated against the diagnosis that is provided by medical experts. Our performance in terms of accuracy (∼82%), f1 (84%) and ROC‐AUC score (∼82%) are clear indicators of the effectiveness of speech analysis towards detecting cognitive decline. Moreover, our tree‐based classifier produces probability scores that closely follow the proportion of pathological cases in the input data, with a correlation of 94%.ConclusionIn the current evaluation we verified our conjectures regarding the strong capacity of speech to predict cognitive decline. Audio analysis and machine learning are proven to be invaluable tools in the prediction of early signs of cognitive decline, which are coupled with a wide spectrum of neurodegenerative and psychiatric diseases.[1] Boschi, Veronica, et al., Frontiers in psychology 8 (2017): 269.[2] Vassiliki Rentoumi et al., Alzheimer's &amp; Dementia, Wiley, volume 16, 2020.[3] Alberdi, Ane et al., Artificial intelligence in medicine 71 (2016): 1‐29.

Monitoring physical parameters from wearable sensors for detection of cognitive decline in routine care

AbstractBackgroundWearable sensors offer the chance to measure activity and vital signs continuously and unobtrusively in elderly people living in domesticity or stationary care. To derive meaningful features from raw data and transform sensor data into clinically relevant information it is necessary to develop efficient algorithms and validate the results, e.g. with synchronized observational data. Only then sensor data bear the potential to detect functional changes or specific behaviors that are linked to Alzheimer’s disease and allow the complementation of the diagnostic process with sensor data.MethodInternational data point to a potential role of serious games applications to detect early signs of cognitive decline. Along this line a study of the DZNE uses a comprehensive memory paradigm examining age sensitive decline in a citizen science study performed with digital platforms. Complementary approaches include non‐intrusive assessment of everyday behaviour. We performed several studies to examine the feasibility and potential of mobile sensors in detection of challenging behaviors, spatial disorientation and gait changes to classify people with MCI or dementia and detect situations with need for help.ResultIn a study with dementia‐patients that were wearing mobile sensors continuously over several weeks, we could show that multimodal sensors can be applied long‐term in the setting of PwD living in stationary care. Accelerometry can be used to detect behavioral patterns in dementia patients. We extended the set of sensor data with vital signs (e.g. heart rate, electrodermal activity) and indoor tracking to detect spatial disorientation in an ongoing wayfinding study to classify MCI /dementia based on sensor data.ConclusionMobile sensors will be omnipresent in the following years. With complex algorithms we were able to derive features that allow information about crucial personal conditions with respect to mental health. Digital markers including sensor data won’t be a stand‐alone diagnostic tool and need to be embedded in an innovative diagnostic pathway.

Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment.

Detecting early signs of cognitive decline is crucial for early detection and treatment of Alzheimer's Disease. Most of the current screening tools for Alzheimer's Disease represent a significant burden, requiring invasive procedures, or intensive and costly clinical testing. Recent findings have highlighted changes to speech and language patterns that occur in Alzheimer's Disease, and may be detectable prior to diagnosis. Automated tools to assess speech have been developed that can be used on a smartphone or tablet, from one's home, in under 10 min. In this study, we present the results of a study of older adults who completed a digital speech assessment task over a 6-month period. Participants were grouped according to those who scored above (N = 18) or below (N = 18) the recommended threshold for detecting cognitive impairment on the Montreal Cognitive Assessment (MoCA) and those with diagnoses of mild cognitive impairment (MCI) or early Alzheimer's Disease (AD) (N = 14). Older adults who scored above the MoCA threshold had better performance on speech composites reflecting language coherence, information richness, syntactic complexity, and word finding abilities. Those with MCI and AD showed more rapid decline in the coherence of language from baseline to 6-month follow-up, suggesting that this score may be useful both for detecting cognitive decline and monitoring change over time. This study demonstrates that automated speech assessments have potential as sensitive tools to detect early signs of cognitive impairment and monitor progression over time.

Elucidating the association between depression, anxiety, and cognition in middle-aged adults: Application of dimensional and categorical approaches

BackgroundIn older adults, depressive and anxiety symptoms are associated with dementia risk, and represent a manifestation of the dementia prodrome. Understanding how these symptoms are related to cognition in midlife may inform risk models of dementia. MethodsThis study examined the relationship between depressive and anxiety symptoms, and cognition, in a sample (n= 2,657) of participants enrolled in the Healthy Brain Project. Depressive and Anxiety symptoms were assessed using the Depression Anxiety and Stress Scale, Hospital Anxiety and Depression Scale, and centre for Epidemiological Studies Depression Scale. Objective cognition was assessed using the Cogstate Brief Battery and subjective cognition assessed using the Alzheimer's disease Cooperative Study Cognitive Function Instrument. ResultsSomatic- and panic-related anxiety symptoms were associated significantly with poorer attention; while tension- and panic-related anxiety were associated significantly with poorer memory. Having clinically meaningful anxiety or depressive symptoms was associated with increased subjective cognitive concerns (d=-0.37). This was further increased for those with clinically meaningful anxiety and depressive symptoms (d = -1.07). LimitationsThis study reports cross-sectional data, and uses a sample enriched with individuals with a family history of dementia who are therefore at a higher risk of developing dementia compared to the general population. Additionally, biological markers such as cortisol, Aβ, and tau were unavailable. ConclusionThe results support the hypothesis that depressive and anxiety symptoms may increase risk of cognitive decline. Further, they suggest that using depression and anxiety as clinical markers may be helpful in identifying the earliest signs of cognitive decline.