scholarly journals Digital Screening for Cognitive Impairment – A Proof of Concept Study

2021 ◽  
pp. 1-8
Author(s):  
V. Bloniecki ◽  
G. Hagman ◽  
M. Ryden ◽  
M. Kivipelto
Keyword(s):  
Cognitive Impairment ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Roc Curves ◽  
University Hospital ◽  
Cognitive Test ◽  
Test Time ◽  
Proof Of Concept ◽  
Cognitive Screening ◽  
Disease Modifying Drugs

Background: Due to an ageing demographic and rapid increase of cognitive impairment and dementia, combined with potential disease-modifying drugs and other interventions in the pipeline, there is a need for the development of accurate, accessible and efficient cognitive screening instruments, focused on early-stage detection of neurodegenerative disorders. Objective: In this proof of concept report, we examine the validity of a newly developed digital cognitive test, the Geras Solutions Cognitive Test (GCST) and compare its accuracy against the Montreal Cognitive Assessment (MoCA). Methods: 106 patients, referred to the memory clinic, Karolinska University Hospital, due to memory complaints were included. All patients were assessed for presence of neurodegenerative disorder in accordance with standard investigative procedures. 66% were diagnosed with subjective cognitive impairment (SCI), 25% with mild cognitive impairment (MCI) and 9% fulfilled criteria for dementia. All patients were administered both MoCA and GSCT. Descriptive statistics and specificity, sensitivity and ROC curves were established for both test. Results: Mean score differed significantly between all diagnostic subgroups for both GSCT and MoCA (p<0.05). GSCT total test time differed significantly between all diagnostic subgroups (p<0.05). Overall, MoCA showed a sensitivity of 0.88 and specificity of 0.54 at a cut-off of <=26 while GSCT displayed 0.91 and 0.55 in sensitivity and specificity respectively at a cut-off of <=45. Conclusion: This report suggests that GSCT is a viable cognitive screening instrument for both MCI and dementia.

Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

2021 ◽  
pp. 155005942110582
Author(s):  
Sophie A. Stewart ◽  
Laura Pimer ◽  
John D. Fisk ◽  
Benjamin Rusak ◽  
Ron A. Leslie ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Diffusion Tensor Imaging ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Neurodegenerative Disorder ◽  
Diffusion Tensor ◽  
Early Stage ◽  
White Matter Integrity

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to established groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

scholarly journals Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores

2022 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Jie Wang ◽  
Zhuo Wang ◽  
Ning Liu ◽  
Caiyan Liu ◽  
Chenhui Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Random Forest ◽  
Mental State ◽  
Mini Mental State Examination ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Test Time ◽  
Cognitive Screening ◽  
State Examination

Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis.

scholarly journals Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

2020 ◽  
Vol 77 (4) ◽  
pp. 1545-1558
Author(s):  
Michael F. Bergeron ◽  
Sara Landset ◽  
Xianbo Zhou ◽  
Tao Ding ◽  
Taghi M. Khoshgoftaar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Performance Metrics ◽  
Early Stage ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Cognitive Screening ◽  
Cross Sectional ◽  
Machine Learning Classification

Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

scholarly journals A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5416
Author(s):  
Fatma El-Zahraa A. El-Gamal ◽  
Mohammed Elmogy ◽  
Ali Mahmoud ◽  
Ahmed Shalaby ◽  
Andrew E. Switala ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Brain Regions ◽  
Computer Aided Diagnosis ◽  
Cortical Region ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10–15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder’s effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Abstract 19350: The Decline of the Montreal Cognitive Test Can Detect Post-stroke Cognitive Decline Determined by a Formal Neuropsychological Evaluation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Kenny Xu ◽  
Catherine Dong ◽  
Christopher Chen
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Screening Tests ◽  
Cognitive Test ◽  
Cognitive Screening ◽  
Post Stroke ◽  
Diagnostic Status ◽  
Cerebrovascular Events ◽  
Moderate Cognitive Impairment ◽  
Ischemic Attack

Objective: We aimed to establish the association of decline in cognitive screening tests scores, the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE), with the decline in neuropsychological diagnostic status from 3-6 months to a year later. Method: Patients with ischemic stroke/ Transient Ischemic Attack (TIA) received the MoCA and MMSE within 14 days after stroke, then 3-6 months and 1 year later. The decline in MoCA and MMSE scores were defined by reduction of 2 points or more in total scores, while stable/improved MoCA scores referred to reduction of MoCA scores less than 2 or improved scores. The decline in neuropsychological diagnostic status was defined by category transition from no cognitive impairment to any cognitive impairment (≥1 domain), from mild cognitive impairment (impairment in 1-2 domains) to moderate cognitive impairment (impairment >2 domains) and dementia (i.e., functional loss associated with cognitive impairment, DSM-IV criteria), as well as from moderate cognitive impairment to dementia. Results: At baseline, most patients were Chinese (70.3%) and males (69.8%) with age of 59.8 ± 11.6 years and education of 7.7 ± 4.3 years. 327 out of 400 stroke/TIA patients completed neuropsychological assessments at 3-6 months and 275 completed at 1 year after their index cerebrovascular events. Of these, 31 (11.3%) had decline in neuropsychological diagnostic status. Logistic regression was used to model the association between probability of decline in neuropsychological diagnostic status and the decline in MMSE or MoCA scores. There were not significant associations between the decline of neuropsychological diagnostic status and the decline in MMSE scores. Controlling baseline MoCA scores and the change scores of MoCA from baseline to 3-6 months, patients with decline in MoCA scores (reduction of 2 points or more) were associated with higher risks of decline in neuropsychological diagnostic status, relative to those with stable/ improved MoCA scores (odd ratio=3.21, p=0.004). Conclusion: The decline in MoCA scores are associated with a higher risks for decline in neuropsychological diagnostic status from 3-6 months to 1 year, therefore may be used to detect post-stroke cognitive decline.

scholarly journals An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-inputbdigital drawing tasks

2021 ◽  
Author(s):  
Natthanan Ruengchaijatuporn ◽  
Itthi Chatnuntawech ◽  
Surat Teerapittayanon ◽  
Sira Sriswasdi ◽  
Sirawaj Itthipuripat ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Healthy Aging ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Aging Population ◽  
Modeling Framework ◽  
Drawing Tasks

Mild cognitive impairment (MCI) is an early stage of age-inappropriate cognitive decline, which could develop into dementia – an untreatable neurodegenerative disorder. An early detection of MCI is a crucial step for timely prevention and intervention. To tackle this problem, recent studies have developed deep learning models to detect MCI and various types of dementia using data obtained from the classic clock-drawing test (CDT), a popular neuropsychological screening tool that can be easily and rapidly implemented for assessing cognitive impairments in an aging population. While these models succeed at distinguishing severe forms of dementia, it is still difficult to predict the early stage of the disease using the CDT data alone. Also, the state-of-the-art deep learning techniques still face the black-box challenges, making it questionable to implement them in the clinical setting. Here, we propose a novel deep learning modeling framework that incorporates data from multiple drawing tasks including the CDT, cube-copying, and trail-making tasks obtained from a digital platform. Using self-attention and soft-label methods, our model achieves much higher classification performance at detecting MCI compared to those of a well-established convolutional neural network model. Moreover, our model can highlight features of the MCI data that considerably deviate from those of the healthy aging population, offering accurate predictions for detecting MCI along with visual explanation that aids the interpretation of the deep learning model.

scholarly journals ReCODE: A Personalized, Targeted, Multi-Factorial Therapeutic Program for Reversal of Cognitive Decline

Biomedicines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1348
Author(s):  
Rammohan V Rao ◽  
Sharanya Kumar ◽  
Julie Gregory ◽  
Christine Coward ◽  
Sho Okada ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Early Stage ◽  
Treatment Strategies ◽  
High Sensitivity ◽  
Rank Test ◽  
Proof Of Concept ◽  
Therapeutic Program

Background: Alzheimer’s disease (AD) is the major cause of age-associated cognitive decline, and in the absence of effective therapeutics is progressive and ultimately fatal, creating a dire need for successful prevention and treatment strategies. We recently reported results of a successful proof-of-concept trial, using a personalized, precision medicine protocol, but whether such an approach is readily scalable is unknown. Objective: In the case of AD, there is not a single therapeutic that exerts anything beyond a marginal, unsustained, symptomatic effect. This suggests that the monotherapeutic approach of drug development for AD may not be an optimal one, at least when used alone. Using a novel, comprehensive, and personalized therapeutic system called ReCODE (reversal of cognitive decline), which proved successful in a small, proof-of-concept trial, we sought to determine whether the program could be scaled to improve cognitive and metabolic function in individuals diagnosed with subjective cognitive impairment, mild cognitive impairment, and early-stage AD. Methods: 255 individuals submitted blood samples, took the Montreal Cognitive Assessment (MoCA) test, and answered intake questions. Individuals who enrolled in the ReCODE program had consultations with clinical practitioners, and explanations of the program were provided. Participants had follow-up visits that included education regarding diet, lifestyle choices, medications, supplements, repeat blood sample analysis, and MoCA testing between 2 and 12 months after participating in the ReCODE program. Pre- and post-treatment measures were compared using the non-parametric Wilcoxon signed rank test. Results and Conclusions: By comparing baseline to follow-up testing, we observed that MoCA scores either significantly improved or stabilized in the entire participant pool—results that were not as successful as those in the proof-of-concept trial, but more successful than anti-amyloid therapies—and other risk factors including blood glucose, high-sensitivity C-reactive protein, HOMA-IR, and vitamin D significantly improved in the participant pool. Our findings provide evidence that a multi-factorial, comprehensive, and personalized therapeutic program designed to mitigate AD risk factors can improve risk factor scores and stabilize or reverse the decline in cognitive function. Since superior results were obtained in the proof-of-concept trial, which was conducted by a small group of highly trained and experienced physicians, it is possible that results from the use of this personalized approach would be enhanced by further training and experience of the practicing physicians. Nonetheless, the current results provide further support indicating the potential of such an approach for the prevention and reversal of cognitive decline.

scholarly journals Dual-task tests discriminate between dementia, mild cognitive impairment, subjective cognitive impairment, and healthy controls – a cross-sectional cohort study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hanna B. Åhman ◽  
Ylva Cedervall ◽  
Lena Kilander ◽  
Vilmantas Giedraitis ◽  
Lars Berglund ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Dual Task ◽  
Early Stage ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Timed Up And Go ◽  
Cross Sectional ◽  
Single Task ◽  
Subjective Cognitive Impairment

Abstract Background Discrimination between early-stage dementia and other cognitive impairment diagnoses is central to enable appropriate interventions. Previous studies indicate that dual-task testing may be useful in such differentiation. The objective of this study was to investigate whether dual-task test outcomes discriminate between groups of individuals with dementia disorder, mild cognitive impairment, subjective cognitive impairment, and healthy controls. Methods A total of 464 individuals (mean age 71 years, 47% women) were included in the study, of which 298 were patients undergoing memory assessment and 166 were cognitively healthy controls. Patients were grouped according to the diagnosis received: dementia disorder, mild cognitive impairment, or subjective cognitive impairment. Data collection included participants’ demographic characteristics. The patients’ cognitive test results and diagnoses were collected from their medical records. Healthy controls underwent the same cognitive tests as the patients. The mobility test Timed Up-and-Go (TUG single-task) and two dual-task tests including TUG (TUGdt) were carried out: TUGdt naming animals and TUGdt months backwards. The outcomes registered were: time scores for TUG single-task and both TUGdt tests, TUGdt costs (relative time difference between TUG single-task and TUGdt), number of different animals named, number of months recited in correct order, number of animals per 10 s, and number of months per 10 s. Logistic regression models examined associations between TUG outcomes pairwise between groups. Results The TUGdt outcomes “animals/10 s” and “months/10 s” discriminated significantly (p < 0.001) between individuals with an early-stage dementia diagnosis, mild cognitive impairment, subjective cognitive impairment, and healthy controls. The TUGdt outcome “animals/10 s” showed an odds ratio of 3.3 (95% confidence interval 2.0–5.4) for the groups dementia disorders vs. mild cognitive impairment. TUGdt cost outcomes, however, did not discriminate between any of the groups. Conclusions The novel TUGdt outcomes “words per time unit”, i.e. “animals/10 s” and “months/10 s”, demonstrate high levels of discrimination between all investigated groups. Thus, the TUGdt tests in the current study could be useful as complementary tools in diagnostic assessments. Future studies will be focused on the predictive value of TUGdt outcomes concerning dementia risk for individuals with mild cognitive impairment or subjective cognitive impairment.

scholarly journals Resting heart rate variability as a possible marker of cognitive decline

Kinesiology ◽  
2020 ◽  
Vol 52 (1) ◽  
pp. 72-84
Author(s):  
Bernhard Grässler ◽  
Anita Hökelmann ◽  
Richard Halti Cabral
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Cognitive Impairment ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Cognitive Performance ◽  
Processing Speed ◽  
Early Stage ◽  
Cognitive Test

Cognition is a major subject to be addressed nowadays due to the increasing number of cognitively affected people in most societies. Because of a lack of pharmaceutical therapies treating cognitive decline, its indicators should be diagnosed before it becomes prevalent. Scientific evidence indicates a relationship between cognition and the nervous system, especially its autonomic part. Heart rate variability (HRV) as an indicator of the autonomic nervous system functioning has been studied as a biological marker for the evaluation of cognitive performance. Therefore, HRV is a possible indicator of cognitive impairment. The aim was to provide a systematic literature review about the association between resting HRV and the cognitive performance. Five cognitive functions were analysed separately: executive functions, memory and learning, language abilities, visuospatial functioning, and processing speed. Furthermore, the global cognitive function evaluated with cognitive test batteries was considered too. An electronic database search was conducted with five databases. Three search fields comprised HRV, cognitive performance, and adult subjects. The final dataset consisted of 27 articles. Significant correlations in each cognitive function were found, except for processing speed, suggesting a positive association between resting HRV and cognitive performance. Mechanisms underlying this association between cardiovascular health and cognition are discussed. For the future, HRV could be used in diagnostics as an indicator of cognitive impairment before symptoms of dementia get apparent. With a timely diagnosis, preventative tools could be initiated at an early stage of dementia.

scholarly journals Cognitive Change Questionnaire as a method for cognitive impairment screening

2015 ◽  
Vol 9 (3) ◽  
pp. 237-244 ◽  
Author(s):  
Antonio Eduardo Damin ◽  
Ricardo Nitrini ◽  
Sonia Maria Dozzi Brucki
Keyword(s):  
Cognitive Impairment ◽  
Good Accuracy ◽  
Early Stage ◽  
Normal Subjects ◽  
Final Diagnosis ◽  
Cognitive Change ◽  
Individual Performance ◽  
Cognitive Screening ◽  
Effective Measure ◽  
Selection Of

The Cognitive Change Questionnaire (CCQ) was created as an effective measure of cognitive change that is easy to use and suitable for application in Brazil. Objective: To evaluate whether the CCQ can accurately distinguish normal subjects from individuals with Mild Cognitive Impairment (MCI) and/or early stage dementia and to develop a briefer questionnaire, based on the original 22-item CCQ (CCQ22), that contains fewer questions. Methods: A total of 123 individuals were evaluated: 42 healthy controls, 40 patients with MCI and 41 with mild dementia. The evaluation was performed using cognitive tests based on individual performance and on questionnaires administered to informants. The CCQ22 was created based on a selection of questions that experts deemed useful in screening for early stage dementia. Results: The CCQ22 showed good accuracy for distinguishing between the groups. Statistical models selected the eight questions with the greatest power to discriminate between the groups. The AUC ROC corresponding to the final version of the 8-item CCQ (CCQ8), demonstrated good accuracy in differentiating between groups, good correlation with the final diagnosis (r=0.861) and adequate internal consistency (Cronbach's α=0.876). Conclusion: The CCQ8 can be used to accurately differentiate between normal subjects and individuals with cognitive impairment, constituting a brief and appropriate instrument for cognitive screening.

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