Dataset Information

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

ABSTRACT:

Introduction

The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).

Method

Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables.

Results

Classifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone.

Conclusion

The brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.

SUBMITTER: Clark DG

PROVIDER: S-EPMC4879664 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

Clark David Glenn DG McLaughlin Paula M PM Woo Ellen E Hwang Kristy K Hurtz Sona S Ramirez Leslie L Eastman Jennifer J Dukes Reshil-Marie RM Kapur Puneet P DeRamus Thomas P TP Apostolova Liana G LG

Alzheimer's & dementia (Amsterdam, Netherlands) 20160215

<h4>Introduction</h4>The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).<h4>Method</h4>Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine le ...[more]

PMID: 27239542

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Project description:Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment-subtypes amnestic (aMCI) and amnestic multiple domain (a+mdMCI)-and patients with Alzheimer's disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGA). The individuals were compared when divided in three (NC-MCI-AD) and four (NC-aMCI-a+mdMCI-AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Project description:ObjectiveThe current study was designed to achieve 2 primary objectives: (a) determine the moderating effect of mild cognitive impairments (MCI) on intraindividual variability in semantic and letter fluency performance taking into account longitudinal annual assessments; and (b) establish predictive utility for intraindividual variability in semantic and letter fluency performance as a risk factor of incident MCI.MethodsParticipants were community-residing older adults (n = 514; M age = 75.89 ± 6.45; 55.1% female). Sixty participants were diagnosed with MCI at baseline and 50 developed incident MCI during the follow-up. We operationalized intraindividual variability as word generation slopes derived from 3 consecutive time intervals during the standard 1-min administration of both letter and semantic fluency tasks (i.e., 0-20 s, 21-40 s, and 41-60 s).ResultsLinear mixed effects models revealed significant within task slope effects for semantic (estimate = -8.350; p < .0001; 95% confidence interval [CI] = -8.604, -8.095) and letter (estimate = -5.068; p < .0001; 95% CI [-5.268, -4.869]) fluency indicating that word generation declined over the course of both tasks. The two-way interactions of MCI × Slope were significant for semantic (estimate = 1.34; p = .001; 95% CI [0.551, 2.126]) and letter (estimate = 0.733; p = .020; 95% CI [0.116, 1.350]) fluency indicating attenuated slopes among MCI participants compared to controls taking into account repeated annual assessments. Cox proportional-hazards models revealed that attenuated word generation slope, at baseline, in semantic (hazard ratio [HR] = 2.063; p = .015; 95% = 1.149 to 3.702) but not letter (HR = 0.704; p = .243; 95% CI [0.391, 1.269]) fluency was associated with increased risk of incident MCI.ConclusionIntraindividual variability in verbal fluency performance has clinical and predictive utility; it can be easily incorporated into testing batteries in clinical and research settings. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Dataset Information

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

Introduction

Method

Results

Conclusion

Publications

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

Similar Datasets

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