Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment

ABSTRACT: 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.

SUBMITTER: Robin J

PROVIDER: S-EPMC8579012 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Vocabulary Size in Speech May Be an Early Indicator of Cognitive Impairment.

Project description:Little is known about the relationship between mild cognitive impairment (MCI) and changes to language abilities. Here, we used the revised Hasegawa Dementia Scale (HDS-R) to identify suspected MCI in elderly individuals. We then analyzed written and spoken narratives to compare the language abilities between study participants with and without MCI in order to explore the relationship between cognitive and language abilities, and to identify a possible indicator for the early detection of MCI and dementia. We recruited 22 people aged 74 to 86 years (mean: 78.32 years; standard deviation: 3.36). The participants were requested to write and talk about one of the happiest events in their lives. Based on HDS-R scores, we divided the participants into 2 groups: the MCI Group comprised 8 participants with a score of 26 or lower, while the Healthy Group comprised 14 participants with a score of 27 or higher. The transcriptions of both written and spoken samples for each participant were used in the measurement of NLP-based language ability scores. Our analysis showed no significant differences in writing abilities between the 2 groups in any of the language ability scores. However, analysis of the spoken narrative showed that the MCI Group had a significantly larger vocabulary size. In addition, analysis of a metric that signified the gap in content between the spoken and written narratives also revealed a larger vocabulary size in the MCI Group. Individuals with early-stage MCI may be engaging in behavior to conceal their deteriorating cognition, thereby leading to a temporary increase in their active spoken vocabulary. These results indicate the possible detection of early stages of reduced cognition before dementia onset through the analysis of spoken narratives.

| S-EPMC4866705 | biostudies-literature

Microstructural white matter changes, not hippocampal atrophy, detect early amnestic mild cognitive impairment.

Project description:BackgroundAlzheimer's disease (AD) is generally considered to be characterized by pathology in gray matter of the brain, but convergent evidence suggests that white matter degradation also plays a vital role in its pathogenesis. The evolution of white matter deterioration and its relationship with gray matter atrophy remains elusive in amnestic mild cognitive impairment (aMCI), a prodromal stage of AD.MethodsWe studied 155 cognitively normal (CN) and 27 'late' aMCI individuals with stable diagnosis over 2 years, and 39 'early' aMCI individuals who had converted from CN to aMCI at 2-year follow up. Diffusion tensor imaging (DTI) tractography was used to reconstruct six white matter tracts three limbic tracts critical for episodic memory function - the fornix, the parahippocampal cingulum, and the uncinate fasciculus; two cortico-cortical association fiber tracts - superior longitudinal fasciculus and inferior longitudinal fasciculus; and one projection fiber tract - corticospinal tract. Microstructural integrity as measured by fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) was assessed for these tracts.ResultsCompared with CN, late aMCI had lower white matter integrity in the fornix, the parahippocampal cingulum, and the uncinate fasciculus, while early aMCI showed white matter damage in the fornix. In addition, fornical measures were correlated with hippocampal atrophy in late aMCI, whereas abnormality of the fornix in early aMCI occurred in the absence of hippocampal atrophy and did not correlate with hippocampal volumes.ConclusionsLimbic white matter tracts are preferentially affected in the early stages of cognitive dysfunction. Microstructural degradation of the fornix preceding hippocampal atrophy may serve as a novel imaging marker for aMCI at an early stage.

| S-EPMC3597581 | biostudies-literature

Digital Cognitive Assessments for Dementia: Digital assessments may enhance the efficiency of evaluations in neurology and other clinics.

Project description: Not available

| S-EPMC8078574 | biostudies-literature

Cognitive Intervention with Musical Stimuli Using Digital Devices on Mild Cognitive Impairment: A Pilot Study.

Project description:The effect of music therapy on cognitive function has been widely reported; however, its clinical implications remain controversial. Performing therapeutic musical activities in groups using individualized instruments can help overcome the issues of engagement and compliance. We aimed to evaluate the effect of a cognitive intervention with musical stimuli using digital devices on mild cognitive impairment (MCI). In this prospective study, 24 patients with MCI (intervention group, 12; and control group, 12) were enrolled. We developed an electronic device with musical instruments and the Song-based Cognitive Stimulation Therapy protocol (SongCST). Patients in the intervention group underwent a 10-week cognitive intervention involving musical stimuli generated by our device. Effect of the intervention on cognitive function was evaluated by the Mini-Mental State Examination-Dementia Screening (MMSE-DS), Montreal Cognitive Assessment-Korean (MOCA-K), and Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB). In the intervention group, MMSE-DS and MOCA-K scores improved significantly after the 10-week intervention. The changes in MOCA-K and CDR-SB scores were significantly different between the intervention and control groups. Our study showed that music therapy with digital devices has a positive effect on the executive function and overall disease severity in patients with MCI. Our study can facilitate individualization of music therapy using digital devices in groups.

| S-EPMC7151070 | biostudies-literature

Unraveling Early Signs of Navigational Impairment in APPswe/PS1dE9 Mice Using Morris Water Maze.

Project description:Mild behavioral deficits, which are part of normal aging, can be early indicators of an impending Alzheimer's disease. Using the APPswe/PS1dE9 (APP/PS1) mouse model of Alzheimer's disease, we utilized the Morris water maze spatial learning paradigm to systematically evaluate mild behavioral deficits that occur during the early stages of disease pathogenesis. Conventional behavioral analysis using this model indicates that spatial memory is intact at 2 months of age. In this study, we used an alternative method to analyze the behavior of mice, aiming to gain a better understanding of the nature of cognitive deficits by focusing on the unsuccessful trials during water maze learning rather than on the successful ones. APP/PS1 mice displayed a higher number of unsuccessful trials during the initial days of training, unlike their wild-type counterparts. However, with repeated trial and error, learning in APP/PS1 reached levels comparable to that of the wild-type mice during the later days of training. Individual APP/PS1 mice preferred a non-cognitive search strategy called circling, which led to abrupt learning transitions and an increased number of unsuccessful trials. These findings indicate the significance of subtle intermediate readouts as early indicators of conditions such as Alzheimer's disease.

| S-EPMC7770143 | biostudies-literature

Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis.

Project description:BackgroundCertain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders.MethodsSpeech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.ResultsDifferent speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores.ConclusionsDifferent NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.

| S-EPMC8581700 | biostudies-literature

Sleep EEG-Based Approach to Detect Mild Cognitive Impairment.

Project description:Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer's disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work relies on an analysis of awake EEG recordings. However, recent studies have suggested that changes in the structure of sleep may lead to cognitive decline. In this work, we propose a sleep EEG-based method for MCI detection, extracting specific features of sleep to characterize neuroregulatory deficit emergent with MCI. This study analyzed the EEGs of 40 subjects (20 MCI, 20 HC) with the developed algorithm. We extracted sleep slow waves and spindles features, combined with spectral and complexity features from sleep EEG, and used the SVM classifier and GRU network to identify MCI. In addition, the classification results of different feature sets (including with sleep features from sleep EEG and without sleep features from awake EEG) and different classification methods were evaluated. Finally, the MCI classification accuracy of the GRU network based on features extracted from sleep EEG was the highest, reaching 93.46%. Experimental results show that compared with the awake EEG, sleep EEG can provide more useful information to distinguish between MCI and HC. This method can not only improve the classification performance but also facilitate the early intervention of AD.

| S-EPMC9045132 | biostudies-literature

Optimizing Subjective Cognitive Decline to Detect Early Cognitive Dysfunction.

Project description:BackgroundThe utility of subjective cognitive decline (SCD) as an indicator of preclinical AD is overshadowed by its inconsistent association with objective cognition.ObjectiveThis study examines if manipulations of SCD measurement affect its association with early cognitive dysfunction characteristic of preclinical AD.MethodsCognitively healthy older adults (n = 110) completed SCD questionnaires that elicited complaints in general, compared to 5 years ago (retrospective SCD) and compared to their peers (age-anchored SCD) in binary and Likert scales. Outcome cognitive tasks included an associative memory task (Face-Name Test), a visual short-term memory binding task (STMB test), and a clinical neuropsychological list learning test (Selective Reminder Test).ResultsSCD complaints, when compared to age-matched peers (age-anchored SCD) were endorsed less frequently than complaints compared to 5 years ago (retrospective SCD) (p < 0.01). In demographically adjusted regressions, age-anchored ordinal-rated SCD was associated with short term memory binding (β= -0.22, p = 0.040, CI = -0.45, -0.01), associative memory (β= -0.26, p = 0.018, CI = -0.45, -0.06), and list learning (β= -0.31, p = 0.002, CI = -0.51, -0.12). Retrospective and general ordinal-rated SCD was associated with associative memory (β= -0.25, p = 0.012, CI = -0.44, -0.06; β= -0.29, p = 0.003, CI = -0.47, -0.10) and list learning only (β= -0.25, p = 0.014, CI = -0.45, -0.05; β= -0.28, p = 0.004, CI = -0.48, -0.09).ConclusionOrdinal age-anchored SCD appears better suited than other SCD measurements to detect early cognitive dysfunction characteristic of preclinical AD.

| S-EPMC8043980 | biostudies-literature

Neurological soft signs and cognitive performance in early childhood.

Project description:Neurological soft signs (NSSs), minor neurological abnormalities, have been implicated as risk factors for poor cognitive performance in small-scale studies. Here we investigate the associations between NSSs and multiple domains of cognitive performance in a large, population-based cohort and evaluate sex differences in these associations. We analyzed data from 35,710 seven-year-old children in the Collaborative Perinatal Project to study the association between the number of NSSs and cognitive test scores using multiple linear regression models adjusting for risk factors for brain injury and aberrant neurodevelopment. NSSs were associated with lower scores on all domains of cognitive performance. Each additional soft sign was associated with lower full-scale IQ (b = -4.83, 95% CI [-5.06, -4.60]), performance IQ (b = -4.28, 95% CI [-4.54, -4.02]), and verbal IQ scores (b = -4.53, 95% CI [-4.76, -4.30]), as well as arithmetic (b = -4.06, 95% CI [-4.26, -3.85]), spelling (b = -3.53, 95% CI [-3.75, -3.30]), and reading (b = -4.00, 95% CI [-4.26, -3.75]) scores on the Wide Range Achievement Test (WRAT). Only one sex difference was observed: The association between NSS and the WRAT spelling test was somewhat stronger in girls (b = -4.01, 95% CI [-4.26, -3.36]) than in boys (b = -3.53, 95% CI [-3.75, -3.30]). There is an association between NSSs and poor cognitive performance that is not attributable to established risk factors for brain injury and aberrant neurodevelopment. Further research is needed to investigate the mechanisms underlying this association and to determine if interventions targeting NSS in children might have beneficial effects on neurocognitive development. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

| S-EPMC6202138 | biostudies-literature

Accuracy of Two Cognitive Screening Tools to Detect Mild Cognitive Impairment in Parkinson's Disease.

Project description:BackgroundPatients with Parkinson's disease (PD) who have mild cognitive impairment (PD-MCI) are at increased risk of developing PD dementia (PDD). Therefore, it is important to identify PD-MCI in a reliable way.ObjectivesWe evaluated the accuracy of the Parkinson's Disease-Cognitive Rating Scale (PD-CRS) and the Mattis Dementia Rating Scale-2 (MDRS-2) for detecting PD-MCI. Data from healthy subjects were used to correct for demographic influences.MethodsWe compared the accuracy of the two instruments using ROC analysis. The gold standard was level II diagnosis of PD-MCI according to consensus criteria of the International Parkinson and Movement Disorder Society.ResultsSeventy-five healthy subjects and 125 PD patients were included. Education level, age and sex correlated with the PD-CRS, but only age correlated with the MDRS-2. Twenty-seven percent of the patients had PD-MCI. Areas under the curve (AUCs) for raw scores of PD-CRS and MDRS-2 were 0.83 and 0.81, respectively. At the optimal cut-off for the PD-CRS (101/102), sensitivity was 88% and specificity was 64%. For the MDRS-2 (139/140) sensitivity and specificity were 68% and 79%, respectively.AUCs for demographically corrected scores of PD-CRS and for age-corrected scores of MDRS-2 were 0.80 and 0.78, respectively. At the optimal cut-off for the PD-CRS, sensitivity was 79% and specificity was 72%, while for the MDRS-2 these were 77% and 67%, respectively.ConclusionsBoth cognitive screening tools are suitable for distinguishing PD-MCI patients from cognitively intact PD patients. Demographical correction of scores did not improve sensitivity and specificity.

| S-EPMC6174373 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data