Dataset Information

Linguistic markers predict onset of Alzheimer's disease.

ABSTRACT: Background: The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis.

Methods: To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task. We compared the predictive performance of linguistic variables with clinical and neuropsychological variables. The study included 703 samples from 270 participants out of which a dataset consisting of a single sample from 80 participants was held out for testing. Half of the participants in the test set developed AD symptoms before 85 years old, while the other half did not. All samples in the test set were collected during the cognitively normal period (before MCI). The mean time to diagnosis of mild AD was 7.59 years.

Findings: Significant predictive power was obtained, with AUC of 0.74 and accuracy of 0.70 when using linguistic variables. The linguistic variables most relevant for predicting onset of AD have been identified in the literature as associated with cognitive decline in dementia.

Interpretation: The results suggest that language performance in naturalistic probes expose subtle early signs of progression to AD in advance of clinical diagnosis of impairment.

Funding: Pfizer, Inc. provided funding to obtain data from the Framingham Heart Study Consortium, and to support the involvement of IBM Research in the initial phase of the study. The data used in this study was supported by Framingham Heart Study's National Heart, Lung, and Blood Institute contract (N01-HC-25195), and by grants from the National Institute on Aging grants (R01-AG016495, R01-AG008122) and the National Institute of Neurological Disorders and Stroke (R01-NS017950).

SUBMITTER: Eyigoz E

PROVIDER: S-EPMC7700896 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Linguistic markers predict onset of Alzheimer's disease.

Eyigoz Elif E Mathur Sachin S Santamaria Mar M Cecchi Guillermo G Naylor Melissa M

EClinicalMedicine 20201022

<h4>Background</h4>The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis.<h4>Methods</h4>To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-descriptio ...[more]

PMID: 33294808

Similar Datasets

Project description:See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

Project description:PurposeEarly cognitive symptoms such as word-finding difficulty (WFD) in daily conversation are common in Parkinson's disease (PD), but studies have been limited by a lack of feasible, quantitative measures. Linguistic analysis, focused on pauses in speech, may yield markers of impairment of cognition and communication in PD. The objective of this study was to evaluate the relationship of linguistic markers in semistructured speech to WFD symptoms and cognitive function in PD.MethodSpeech recordings of description of the Cookie Theft picture in 53 patients with PD without dementia and 23 elderly controls were analyzed with Praat software. Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), category naming fluency, and confrontation naming tests were administered. Questionnaires rating WFD symptoms and cognitive instrumental activities of daily living were completed. We determined the relationships between (a) pause length and location, (b) MoCA score, and (c) WFD symptoms, using Pearson's correlations and multivariate regression models.ResultsCompared with controls, patients with PD had more pauses within utterances as well as fewer words per minute and a lower percentage of well-formed sentences. Pauses within utterances differed significantly between PD-mild cognitive impairment and normal cognition (p < .001). Words per minute and percentage of well-formed sentences were predictive of MoCA in multivariate regression models. Pauses before verbs were associated with patient-reported severity of WFD symptoms (p = .006).ConclusionsLinguistic markers including pauses within utterances distinguish patients with PD with mild cognitive symptoms from elderly controls. These markers are associated with global cognitive function before the onset of dementia. Pauses before verbs and grammatical markers may index early cognitive symptoms such as WFD that may interfere with functional communication.Supplemental materialhttps://doi.org/10.23641/asha.6615401.

Dataset Information

Linguistic markers predict onset of Alzheimer's disease.

Publications

Linguistic markers predict onset of Alzheimer's disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets