LINGUISTIC MEASURES OF SPOKEN UTTERANCES FOR DETECTING MILD COGNITIVE IMPAIRMENT
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ABSTRACT:
SUBMITTER: Asgari M
PROVIDER: S-EPMC6844676 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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Project description:INTRODUCTION:Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial. METHODS:Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI (ClinicalTrials.gov: NCT01571427). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants. RESULTS:MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%. DISCUSSION:Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.
| S-EPMC5651423 | biostudies-literature
Project description:Conversation is a complex cognitive task that engages multiple aspects of cognitive functions to remember the discussed topics, monitor the semantic and linguistic elements, and recognize others' emotions. In this paper, we propose a computational method based on the lexical coherence of consecutive utterances to quantify topical variations in semi-structured conversations of older adults with cognitive impairments. Extracting the lexical knowledge of conversational utterances, our method generates a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verify the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.
| S-EPMC7909094 | biostudies-literature
Project description:BackgroundSensitive measures of cognition are needed in preclinical and prodromal Alzheimer's disease (AD) to track cognitive change and evaluate potential interventions. Neurofibrillary tangle pathology in AD is first observed in Brodmann Area 35 (BA35), the medial portion of the perirhinal cortex. The importance of the perirhinal cortex for semantic memory may explain early impairments of semantics in preclinical AD. Additionally, our research has tied figurative language impairment to neurodegenerative disease.ObjectiveWe aim to identify tasks that are sensitive to cognitive impairment in individuals with mild cognitive impairment (MCI), and that are sensitive to atrophy in BA35.MethodsIndividuals with MCI and cognitively normal participants (CN) were tested on productive and receptive experimental measures of semantic memory and experimental tests of figurative language comprehension (including metaphor and verbal analogy). Performance was related to structural imaging and standard neuropsychological assessment.ResultsOn the experimental tests of semantics and figurative language, people with MCI performed worse than CN participants. The experimental semantic memory tasks are sensitive and specific; performance on the experimental semantic memory tasks related to medial temporal lobe structural integrity, including BA35, while standard neuropsychological assessments of semantic memory did not, demonstrating the sensitivity of these experimental measures. A visuo-spatial analogy task did not differentiate groups, confirming the specificity of semantic and figurative language tasks.ConclusionThese experimental measures appear sensitive to cognitive change and neurodegeneration early in the AD trajectory and may prove useful in tracking cognitive change in clinical trials aimed at early intervention.
| S-EPMC8822438 | biostudies-literature
Project description:Although subtle discourse declines in people with mild cognitive impairment (MCI) have been reported, heterogeneous measures and tasks among the MCI discourse studies have yielded widely varying outcomes. The present study aimed to first, identify discourse measures that aid the differentiation among people with amnestic MCI (aMCI), people with non-amnestic MCI (naMCI), and cognitively healthy control (HC) participants, and second, delineate the cognitive functions related to such discourse measures. Three discourse tasks (an episodic narrative, a planning task, and a picture description) were performed by 30 aMCI, 22 naMCI, and 21 HC participants. Samples were analyzed using six categories of 15 measures, namely coherence, cohesion, proposition, grammaticality, lexicality, and fluency. The statistical analyses included (1) a multivariate analysis of variance for group comparison; (2) binary simple logistic regression and receiver operating characteristic curve analysis for differentiation between two groups; (3) binary multiple logistic regression for being diagnosed with naMCI or aMCI with the minimum number of predictors; and (4) Pearson correlation analysis for identifying the cognitive functions associated with the discourse measures. The proportion of cohesive words and propositional density in aMCI participants were worse than those in naMCI participants. Global coherence, the proportion of cohesive words, and the proportion of dysfluencies and pauses in naMCI participants were lower than those in the HC participants. Global and local coherence and the proportion of cohesive words, cohesive ties per utterances, propositional density, and dysfluencies and pauses in aMCI participants were worse than those in the HC participants. The aforementioned measures were demonstrated to be effective predictors for classifying groups by receiver operating characteristic curve analysis. In addition, the proportions of cohesive words and pauses were common discourse measures for differentiation between naMCI and HC participants or between aMCI and HC participants using binary multiple logistic regression. According to the correlation analysis, memory and executive functions are needed for coherent, cohesive, and efficient discourse productions in MCI. The detailed description of discourse performances in this study will aid the characterization of the declined language abilities of MCI participants and also the understanding of the cognitive functions involved in discourse performance in MCI.
| S-EPMC6714864 | biostudies-literature
Project description:There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD). We hypothesized that an independent linguistic battery comprising of only the language components or subtests of popular test batteries could give a better clinical diagnosis for MCI-AD compared to using an exhaustive battery of tests. As such, we combined multiple clinical datasets and performed Exploratory Factor Analysis (EFA) to extract the underlying linguistic constructs from a combination of the Consortium to Establish a Registry for Alzheimer's disease (CERAD), Wechsler Memory Scale (WMS) Logical Memory (LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic variables with distinct underlying linguistic constructs that show Cronbach's alpha of 0.74 on the MCI-AD group and 0.87 on the healthy control group. Our machine learning evaluation showed a robust AUC of 0.97 when controlled for age, sex, race, and education, and a clinically reliable AUC of 0.88 without controlling for age, sex, race, and education. Overall, the linguistic battery showed a better diagnostic result compared to the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR), and a combination of MMSE and CDR.
| S-EPMC7058300 | biostudies-literature
Project description:In recent years, behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI) using conversations. While the combination of linguistic and acoustic signals have been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we show that linguistic and acoustic features can be combined synergistically to identify MCI in semi-structured conversations. Using conversational data from an on-going clinical trial (Clinicaltrials.gov: NCT02871921), we find that the combination of linguistic and acoustic features on semi-structured conversations achieves a mean AUC of 82.7, significantly (p < 0.01) out-performing linguistic-only (74.9 mean AUC) or acoustic-only (65.0 mean AUC) detections on hold-out data. Additionally, features (linguistic, acoustic and combination) obtained from semi-structured conversations outperform their counterparts obtained from structured weekly conversations in identifying MCI. Some linguistic categories are significantly better at predicting MCI status (e.g., death, home) than others.
| S-EPMC8878676 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-08-05 | E-GEOD-63063 | biostudies-arrayexpress
Project description:To assess whether single-time-point and longitudinal volumetric magnetic resonance (MR) imaging measures provide predictive prognostic information in patients with amnestic mild cognitive impairment (MCI).This study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from all participants or the participants' legal guardians. Cross-validated discriminant analyses of MR imaging measures were performed to differentiate 164 Alzheimer disease (AD) cases from 203 healthy control cases. Separate analyses were performed by using data from MR images obtained at one time point or by combining single-time-point measures with 1-year change measures. Resulting discriminant functions were applied to 317 MCI cases to derive individual patient risk scores. Risk of conversion to AD was estimated as a continuous function of risk score percentile. Kaplan-Meier survival curves were computed for risk score quartiles. Odds ratios (ORs) for the conversion to AD were computed between the highest and lowest quartile scores.Individualized risk estimates from baseline MR examinations indicated that the 1-year risk of conversion to AD ranged from 3% to 40% (average group risk, 17%; OR, 7.2 for highest vs lowest score quartiles). Including measures of 1-year change in global and regional volumes significantly improved risk estimates (P = 001), with the risk of conversion to AD in the subsequent year ranging from 3% to 69% (average group risk, 27%; OR, 12.0 for highest vs lowest score quartiles).Relative to the risk of conversion to AD conferred by the clinical diagnosis of MCI alone, MR imaging measures yield substantially more informative patient-specific risk estimates. Such predictive prognostic information will be critical if disease-modifying therapies become available.http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101975/-/DC1.
| S-EPMC3099042 | biostudies-other
Project description:Objective:The aim of this study was to validate the reliability of the Chinese version of Addenbrooke's Cognitive Examination III (ACE-III) for detecting mild cognitive impairment. Furthermore, the present study compares the diagnostic accuracy of ACE-III with that of Montreal Cognitive Assessment (MoCA). Methods:One hundred and twenty patients with MCI and 136 healthy controls were included in the study. All patients were evaluated by the Chinese version of ACE-III, MoCA and MMSE. Results:Subjects in the control group showed better performance in ACE-III total score and its subdomain scores than those in the MCI group. There was a significantly positive correlation between ACE-III total score and MoCA score. Meanwhile, there was also a significantly positive correlation between ACE-III total score and MMSE score. For ACE-III total score, a cut-off point of 85 yielded a sensitivity of 97.3% and a specificity of 90.7%. The AUC for ACE-III total score was 0.978. For MoCA, a cut-off point of 23 yielded a sensitivity of 86.5% and a specificity of 97.7%. The AUC for MoCA was 0.961. There were no significant differences in diagnostic accuracy between ACE-III and MoCA. Conclusion:The present findings support that both ACE-III and MoCA are useful for detecting MCI in early stages.
| S-EPMC6750842 | biostudies-literature
Project description:The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson's Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial-temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.
| S-EPMC9963713 | biostudies-literature