Classification Of Cognitive Impairment Research Articles

Classification of Cognitive Impairment Using Quadratic Discriminant Analysis Based Spiral Dynamic Optimization Algorithm

Classification of Cognitive Impairment Using Quadratic Discriminant Analysis Based Spiral Dynamic Optimization Algorithm

Classification of cognitive impairment using electroencephalography for clinical inspection.

Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.

A Transformer Approach for Cognitive Impairment Classification and Prediction.

Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems. We analyzed 3 target feature-sets from the National Alzheimer Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)-dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants' future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance. We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD). The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.

Clinical classification of memory and cognitive impairment with multimodal digital biomarkers.

Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline. To evaluate multi-modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test-determined memory impairment. Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve=0.83; sensitivity=0.81, specificity=0.80). Multimodal models were superior to all other single modality and demographics models. The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.

Classification of cognitive impairment in older adults based on brain functional state measurement data via hierarchical clustering analysis.

Cognitive impairment (CI) is a common degenerative condition in the older population. However, the current methods for assessing CI are not based on brain functional state, which leads to delayed diagnosis, limiting the initiatives towards achieving early interventions. A total of one hundred and forty-nine community-dwelling older adults were recruited. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Exam (MMSE) were used to screen for CI, while brain functional was assessed by brain functional state measurement (BFSM) based on electroencephalogram. Bain functional state indicators associated with CI were selected by lasso and logistic regression models (LRM). We then classified the CI participants based on the selected variables using hierarchical clustering analysis. Eighty-one participants with CI detected by MoCA were divided into five groups. Cluster 1 had relatively lower brain functional states. Cluster 2 had highest mental task-switching index (MTSi, 13.7 ± 3.4), Cluster 3 had the highest sensory threshold index (STi, 29.9 ± 7.7), Cluster 4 had high mental fatigue index (MFi) and cluster 5 had the highest mental refractory period index (MRPi), and external apprehension index (EAi) (21.6 ± 4.4, 35.4 ± 17.7, respectively). Thirty-three participants with CI detected by MMSE were divided into 3 categories. Cluster 1 had the highest introspective intensity index (IIi, 63.4 ± 20.0), anxiety tendency index (ATi, 67.2 ± 13.6), emotional resistance index (ERi, 50.2 ± 11.9), and hypoxia index (Hi, 41.8 ± 8.3). Cluster 2 had the highest implicit cognitive threshold index (ICTi, 87.2 ± 12.7), and cognitive efficiency index (CEi, 213.8 ± 72.0). Cluster 3 had higher STi. The classifications both showed well intra-group consistency and inter-group variability. In our study, BFSM-based classification can be used to identify clinically and brain-functionally relevant CI subtypes, by which clinicians can perform personalized early rehabilitation.

Sensitivity of Individual and Composite Test Scores from the Cogstate Brief Battery to Mild Cognitive Impairment and Dementia Due to Alzheimer's Disease.

The Cogstate Brief Battery (CBB) is a computerized cognitive test battery used commonly to identify cognitive deficits related to Alzheimer's disease (AD). However, AD and normative samples used to understand the sensitivity of the CBB to AD in the clinic have been limited, as have the outcome measures studied. This study investigated the sensitivity of CBB outcomes, including potential composite scores, to cognitive impairment in mild cognitive impairment (MCI) and dementia due to AD, in carefully selected samples. Samples consisted of 4,871 cognitively unimpaired adults and 184 adults who met clinical criteria for MCI (Clinical Dementia Rating (CDR) = 0.5) or dementia (CDR > 0.5) due to AD and CBB naive. Speed and accuracy measures from each test were examined, and theoretically- and statistically-derived composites were created. Sensitivity and specificity of classification of cognitive impairment were compared between outcomes. Individual CBB measures of learning and working memory showed high discriminability for AD-related cognitive impairment for CDR 0.5 (AUCs ∼ 0.79-0.88), and CDR > 0.5 (AUCs ∼ 0.89-0.96) groups. Discrimination ability for theoretically derived CBB composite measures was high, particularly for the Learning and Working Memory (LWM) composite (CDR 0.5 AUC = 0.90, CDR > 0.5 AUC = 0.97). As expected, statistically optimized linear composite measures showed strong discrimination abilities albeit similar to the LWM composite. In older adults, the CBB is effective for discriminating cognitive impairment due to MCI or AD-dementia from unimpaired cognition with the LWM composite providing the strongest sensitivity.

EEG-Based Functional Connectivity Analysis for Cognitive Impairment Classification

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios.

Early rise of glutamate-glutamine levels in mild cognitive impairment: Evidence for emerging excitotoxicity

BackgroundUse proton magnetic resonance spectroscopy (1H-MRS) non invasive technique to assess the modifications of glutamate-glutamine (Glx) and gammaaminobutyric acid (GABA) brain levels in patients reporting a cognitive complain MethodsPosterior cingular cortex 1H-MRS spectra of 46 patients (19 male, 27 female) aged 57 to 87 years (mean : 73.32 ± 7.33 years) with a cognitive complaint were examined with a MEGA PRESS sequence at 3T, and compounds Glutamateglutamine (Glx), GABA, Creatine (Cr) and NAA were measured. From this data the metabolite ratios Glx/Cr, GABA/Cr and NAA/Cr were calculated. In addition, all patient performed the Mini Mental State Evaluation (MMSE) and 2 groups were realized with the clinical threshold of 24. Results16 patients with MMSE 〈 24 and 30 patients with MMSE 〉 24. Significant increase of Glx/Cr in PCC of patients with MMSE 〈 24 compared to patients with MMSE 〉 24. Moreover, GABA/Cr ratio exhibited a trend for a decrease in PCC between the two groups, while they showed a significant decrease NAA/Cr ratio. ConclusionOur results concerning Glx are in agreement with a physiopathological hypothesis involving a biphasic variation of glutamate levels associated with excitotoxicity, correlated with the clinical evolution of the disease. These observations suggest that MRS assessment of glutamate levels could be helpful for both diagnosis and classification of cognitive impairment in stage.

Walking path images from real-time location data predict degree of cognitive impairment

BackgroundWe propose a novel approach that uses spatial walking patterns produced by real-time location systems to classify the severity of cognitive impairment (CI) among residents of a memory care unit. MethodsEach participant was classified as “No-CI”, “Mild-Moderate CI” or “Severe CI” based on their Mini-Mental State Examination scores. The location data was distributed into windows of various durations (5, 10, 15 and 30 min) and transformed into images used to train a custom convolutional neural network (CNN) at each window size. Class Activation Mapping was applied to the top-performing models to determine the features of images associated with each class. ResultsThe best performing model achieved an accuracy of 87.38 % (30-min window length) with an overall pattern that larger window sizes perform better. The class activation maps were effectively consolidated into a Cognitive Impairment Classification Value (CICV) score that distinguishes between No-CI, Mild-Moderate CI, and Severe CI. ConclusionThe class activation maps show that the CNN made relevant and intuitive distinctions for paths corresponding to each class. Future work should validate the proposed techniques with participants who are well-characterized clinically, over larger and diversified settings, and towards classification of neuropsychiatric symptoms such as motor agitation, mood, or apathy.

Cognitive impairment in people living with HIV: consensus recommendations for a new approach.

Current approaches to classifying cognitive impairment in people living with HIV can overestimate disease burden and lead to ambiguity around disease mechanisms. The 2007 criteria for HIV-associated neurocognitive disorders (HAND), sometimes called the Frascati criteria, can falsely classify over 20% of cognitively healthy individuals as having cognitive impairment. Minimum criteria for HAND are met on the basis of performance on cognitive tests alone, which might not be appropriate for populations with diverse educational and socioeconomic backgrounds. Imprecise phenotyping of cognitive impairment can limit mechanistic research, biomarker discovery and treatment trials. Importantly, overestimation of cognitive impairment carries the risk of creating fear among people living with HIV and worsening stigma and discrimination towards these individuals. To address this issue, we established the International HIV-Cognition Working Group, which is globally representative and involves the community of people living with HIV. We reached consensus on six recommendations towards a new approach for diagnosis and classification of cognitive impairment in people living with HIV, intended to focus discussion and debate going forward. We propose the conceptual separation of HIV-associated brain injury - including active or pretreatment legacy damage - from other causes of brain injury occurring in people living with HIV. We suggest moving away from a quantitative neuropsychological approach towards an emphasis on clinical context. Our recommendations are intended to better represent the changing profile of cognitive impairment in people living with HIV in diverse global settings and toprovide a clearer framework of classification for clinical management and research studies.

Behavioral and biologic characteristics of cancer-related cognitive impairment biotypes

Psychiatric diagnosis is moving away from symptom-based classification and towards multi-dimensional, biologically-based characterization, or biotyping. We previously identified three biotypes of chemotherapy-related cognitive impairment based on functional brain connectivity. In this follow-up study of 80 chemotherapy-treated breast cancer survivors and 80 non-cancer controls, we evaluated additional factors to help explain biotype expression: neurofunctional stability, brain age, apolipoprotein (APOE) genotype, and psychoneurologic symptoms. We also compared the discriminative ability of a traditional, symptom-based cognitive impairment definition with that of biotypes. We found significant differences in cortical brain age (F = 10.50, p < 0.001), neurofunctional stability (F = 2.83, p = 0.041), APOE e4 genotype (X2 = 7.68, p = 0.050), and psychoneurological symptoms (Pillai = 0.378, p < 0.001) across the three biotypes. The more resilient Biotype 2 demonstrated significantly higher neurofunctional stability compared to the other biotypes. Symptom-based classification of cognitive impairment did not differentiate biologic or other behavioral variables, suggesting that traditional categorization of cancer-related cognitive effects may miss important characteristics which could inform targeted treatment strategies. Additionally, biotyping, but not symptom-typing, was able to distinguish survivors with cognitive versus psychological effects. Our results suggest that Biotype 1 survivors might benefit from first addressing symptoms of anxiety and fatigue, Biotype 3 might benefit from a treatment plan which includes sleep hygiene, and Biotype 2 might benefit most from cognitive skills training or rehabilitation. Future research should include additional demographic and clinical information to further investigate biotype expression related to risk and resilience and examine integration of more clinically feasible imaging approaches.

Sensory impairment and algorithmic classification of early cognitive impairment.

Sensory impairment (SI) is linked to cognitive decline, but its association with early cognitive impairment (ECI) is unclear. Sensory functions (vision, hearing, vestibular function, proprioception, and olfaction) were measured between 2012 and 2018 in 414 Baltimore Longitudinal Study of Aging (BLSA) participants (age 74±9 years; 55% women). ECI was defined as 1 standard deviation below age-, sex-, race-, and education-specific mean performance in Card Rotations or California Verbal Learning Test immediate recall. Log binomial models (cross-sectional analysis) and Cox regression models (time-to-event analysis) were used to examine the association between SI and ECI. Cross-sectionally, participants with ≥3 SI had twice the prevalence of ECI (prevalence ratio=2.10, p=0.02). Longitudinally, there was no significant association between SI and incident ECI over up to 6 years of follow-up. SI is associated with higher prevalence, but not incident ECI. Future studies with large sample sizes need to further elucidate the relationship between SI and ECI. Sensory impairment is associated with high prevalence of early cognitive impairmentMultisensory impairment may pose a strong risk of early changes in cognitive functionIdentifying multisensory impairment may help early detection of dementia.

Measurement differences in the assessment of functional limitations for cognitive impairment classification across geographic locations.

The measurement of dementia in cross-national contexts relies on the assessment of functional limitations. We aimed to evaluate the performance of survey items on functional limitations across culturally diverse geographic settings. We used data from the Harmonized Cognitive Assessment Protocol Surveys (HCAP) in five countries (total N=11,250) to quantify associations between items on functional limitations and cognitive impairment. Many items performed better in the United States and England compared to South Africa, India, and Mexico. Items on the Community Screening Instrument for Dementia (CSID) had the least variability across countries (SD=0.73vs. 0.92 [Blessed] and 0.98 [Jorm IQCODE]), but also the weakest associations with cognitive impairment (median odds ratio [OR]=2.23vs. 3.01 [Blessed] and 2.75 [Jorm IQCODE]). Differences in cultural norms for reporting functional limitations likely influences performance of items on functional limitations and may affect the interpretation of results from substantive studies. There was substantial cross-country variation in item performance. Items from the Community Screening Instrument for Dementia (CSID) had less cross-country variability but lower performance. There was more variability in performance of instrumental activities of daily living (IADL) compared to activities of daily living (ADL) items. Variability in cultural expectations of older adults should be taken into account. Results highlight the need for novel approaches to assessing functional limitations.

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification.

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects' features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs' input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph's imaging features and edge-assigning functions can both significantly affect classification accuracy.

Subjective Memory Complaints and Their Relationship with the Objective Cognitive Performance of Stroke Patients

Introduction: Subjective memory complaints (SMCs) are common among patients with stroke, significantly affect long-term disability, and contribute to poor functional outcomes. We explored changes in the subjective memory complaints questionnaire (SMCQ) score of stroke patients, correlations among SMCs, objective cognitive performance (OCP), and functional status. We also explored whether participants could be divided into groups based on the presence or absence of SMCs and OCP impairment, which could be related to rehabilitation outcomes. Methods: A total of 102 stroke patients were recruited from a single rehabilitation center. Their OCP was determined on admission. The Mini-Mental State Evaluation (MMSE), SMCQ, and modified Barthel Index (MBI) scores were obtained at admission and at discharge. These variables were compared and time and group interactions were explored. Results: The SMCQ score did not show consistent patterns of change among individuals. The objective cognitive function and activities of daily living consistently improved after rehabilitation. The proposed cognitive impairment classification after stroke based on SMCs and objective cognitive decline was able to predict improvement attributable to rehabilitation. Conclusion: Changes in SMCQ scores of stroke patients were inconsistent and varied when compared to changes in MMSE and MBI scores, indicating that it is not a reliable metric on its own. SMCs have a clinical relationship with OCP and significant emotional and motivational effects. In clinical practice, it is important to understand and consider SMCs after stroke.

Cognitive disorders in patients with cerebrovascular diseases in therapeutic practice: diagnostic and management algorithms

This review summaries the main present-day knowledge on the pathogenesis of cognitive impairement in patients with cardiovascular and cerebrovascular diseases, combining both the development of severe forms such as dementia, and early non-dementia forms of cognitive impairment. Risk factors, including iatrogenic ones, and the mechanisms of development of various forms of cognitive impairment in patients with vascular pathologies are discussed in detail. The definition of human cognitive functions and the modern classification of cognitive impairment used in real clinical practice are given. The clinical aspects of cognitive impairment in the therapeutic practice with a focus on the review of a wide range of specific complaints, reflecting the likelihood of cognitive impairment and non-cognitive symptoms are considered in detail, taking into account the current national clinical guidelines of professional communities. Possible iatrogenic factors that worsen the state of cognitive functions, including an assessment of the anticholinergic drug load on the patient are discussed in detail, and a strategy for identifying and solving this issue as part of a therapeutic approach is provided. An algorithmic approach to the identification of non-dementia cognitive impairment in patients with cerebrovascular pathology is presented by therapeutic specialists working in the primary outpatient healthcare sector. The laboratory and instrumental examination options for patients to identify reversible forms of cognitive impairment are considered in detail. The prospective of choosing preventive non-drug and drug approaches, as well as the therapeutic strategy options for the use of drugs that improve the central nervous system functioning during management of patients with cerebrovascular diseases, depending on the type of cognitive impairment in the general practitioner practice are assessed.

Application of machine learning techniques for dementia severity prediction from psychometric tests in the elderly population

Previous research has shown the benefits of early detection and treatment of dementia. This detection is usually performed manually by one or more clinicians based on reports and psychometric testing. Machine learning algorithms provide an alternative method of prediction that may contribute, with an automated process and insights, to the diagnosis and classification of the severity level of dementia. The aim of this study is to explore the use of neuropsychological data from a reduced version of the Addenbrooke’s Cognitive Examination III (ACE-III) to predict absence or different levels of dementia severity using the Global Deterioration Scale (GDS) scores through the implementation of the kNN machine learning algorithm. A sample of 1164 elderly people over sixty years old were evaluated using a reduced version of the ACE-III and the GDS. The kNN classifier provided good accuracies using 15 items from the ACE-III and adequately differentiating people with absence and mild impairment, from those with more severe levels of impairment according to the GDS rating. Our results suggest that the kNN algorithm may be used to automate aspects of clinical cognitive impairment classification in the elderly population.

Criteria for diagnosis and classification of vascular cognitive impairment

Vascular cognitive impairment (VCI) is a very diverse group of conditions that differ in etiopathogenesis, severity, clinical picture, dynamics and severity of functional disorders. This heterogeneity significantly complicates the development of uniform standardized recommendations for the diagnosis and treatment of VCI. The development of criteria for each clinical and pathogenetic variant for permanent practical application is a difficult task. In this article, based on the analysis of previously proposed approaches to the diagnosis of various clinical and pathogenetic variants, common diagnostic criteria and classification of VCI are presented.

Differences in the measurement of cognition for the assessment of dementia across geographic contexts: Recommendations for cross‐national research

AbstractBackgroundAlthough 71% of individuals with dementia are projected to live and low‐ and middle‐income countries by 2050, most dementia research to‐date has been conducted in high‐income countries. Cross‐national studies are needed to understand how the causes and consequences of dementia may differ by country. However, factors such as urbanicity, language of administration or race/ethnicity, which vary across countries, affect the measurement of cognition. A better understanding of which cognitive items perform well across countries or within specific countries will allow researchers to optimize dementia measurement in future cross‐national and international studies.MethodWe used data from the Harmonized Cognitive Assessment Protocol surveys in the US, Mexico, India, England, and South Africa (combined N = 11,364). To define cognitive impairment consistently across countries, we compared participants’ cognitive performance with robust neuropsychological norms within each country. For each HCAP country, we assessed item performance by estimating associations between each cognitive item and cognitive impairment using logistic regression models, controlling for age and gender. We avoided circularity in the analysis by using an iterative quasi‐leave‐one‐out approach. We compared patterns of associations across countries using median odds ratios, and visually, using heatmaps.ResultThe associations between cognitive items and cognitive impairment were stronger in the US (Median Odds Ratio [OR] = 0.17) and England (Median OR = 0.19), in comparison to South Africa (Median OR = 0.23), India (Median OR = 0.29), and Mexico (Median OR = 0.28). Memory items, notably delayed recall tasks, had the most consistent associations of the largest magnitudes across countries. In comparison, there was variability in performance between settings for many language items (e.g. naming a hammer). Items requiring numeracy, including the Trail‐making test, did not perform well in countries with low educational attainment.ConclusionThe performance of cognitive items for the classification of cognitive impairment was not consistent across countries. Although items performing well in a single country could be used to improve measurement precision in specific settings, transporting items between countries without prior validation warrants caution. However, we did identify items that performed well across a range of countries (i.e. delayed recall, animal naming); these items may be of particular utility in future cross‐national research.

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.