Preserved semantic categorical organization in mild cognitive impairment: A network analysis of verbal fluency

Nevado, Ángel; Río, David del; Martín-Aragoneses, María Teresa; Prados, José M.; López-Higes, Ramón

doi:10.1016/j.neuropsychologia.2021.107875

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…The decline in semantic fluency during aging may originate from both semantic memory degradation and executive function deficits. In a recent meta-analysis that compared MCI participants with NC participants, the results suggest that the semantic network is preserved in MCI, however, existing associations are less efficiently exploited during long-term memory search, possibly due to deficits in the executive function [ 71 ]. In a recent study comparing participants with MCI and AD, it was observed that a significant interaction existed between the groups and the verbal fluency condition (phonemic and semantic).…”

Section: Discussionmentioning

confidence: 99%

Short-term memory, attention, and temporal orientation as predictors of the cognitive impairment in older adults: A cross-sectional observational study

et al. 2021

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Late-life cognitive decline ranges from the mildest cases of normal, age-related change to mild cognitive impairment to severe cases of dementia. Dementia is the largest global burden for the 21st century welfare and healthcare systems. The aim of this study was to analyze the neuropsychological constructs (temporal orientation (TO), spatial orientation (SO), fixation memory (FM), attention (A), calculation (C), short-term memory (STM), language (L), and praxis (P)), semantic fluency, level of functionality, and mood that reveal the greatest deficit in the different stages ranging from normal cognition (NC) to cognitive impairment in older adults in a primary healthcare setting. The study included 337 participants (102 men, 235 women), having a mean age of 74 ± 6 years. According to their scores on the Spanish version of the Mini-Mental State Examination (MEC-35), subjects were divided into 4 groups: no deterioration (ND) (score 32–35), subtle cognitive impairment (SCI) (score 28–31), level deterioration (LD) (score 24–27) and moderate deterioration (MD) (score 20–23). The ND group revealed significant differences in TO, STM, C, A, L, P, and S-T as compared to the other groups. The MD group (in all the neuropsychological constructs) and the ND and SCI groups showed significant differences on the Yesavage geriatric depression scale (GDS-15). All except the FM neuropsychological construct were part of the MEC-35 prediction model and all of the regression coefficients were significant for these variables in the model. Furthermore, the highest average percentage of relative deterioration occurs between LD and MD and the greatest deterioration is observed in the STM for all groups, including A and TO for the LD and MD groups. Based on our findings, community programs have been implemented that use cognitive stimulation to prevent cognitive decline and to maintain the neuropsychological constructs.

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Section: Discussionmentioning

confidence: 99%

Short-term memory, attention, and temporal orientation as predictors of the cognitive impairment in older adults: A cross-sectional observational study

et al. 2021

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“…Trained nurses performed a comprehensive neuropsychological assessment of all patients. The assessment includes general cognition and specific cognitive domains involving the following examinations: (1) global cognition (MMSE and Clinical Dementia Rating-Sum of Boxes [CDR-SB]); (2) processing speed and working memory (Digits Recall Forward and Backward); (3) verbal learning and memory (California Verbal Language Test-II Short Form [CVLT-SF]); (4) semantic verbal fluency (animal naming); (5) language (Boston Naming Test); (6) attention and visuospatial processing (Trail Making Test A and B [TMT-A and TMT-B]); and (7) visuoperceptual and visuospatial processing (Benton Judgement of Line Orientation) [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Cognitive Degeneration in Parkinson’s Disease Patients Using a Machine Learning Method

et al. 2022

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This study developed a predictive model for cognitive degeneration in patients with Parkinson’s disease (PD) using a machine learning method. The clinical data, plasma biomarkers, and neuropsychological test results of patients with PD were collected and utilized as model predictors. Machine learning methods comprising support vector machines (SVMs) and principal component analysis (PCA) were applied to obtain a cognitive classification model. Using 32 comprehensive predictive parameters, the PCA-SVM classifier reached 92.3% accuracy and 0.929 area under the receiver operating characteristic curve (AUC). Furthermore, the accuracy could be increased to 100% and the AUC to 1.0 in a PCA-SVM model using only 13 carefully chosen features.

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“…As the amount of data used can have a large impact on the estimated representations, most studies correct for this, but in idiosyncratic ways. For example, Nevado et al (2021) truncate the length of fluency lists across participants, so that each list is the same length (but perhaps containing different responses). Chan et al (1993) select only a limited set of twelve items that are common between groups to estimate representations, so that each representation contains the same concepts.…”

Section: Unresolved Methodological Issuesmentioning

confidence: 99%

“… Kenett et al (2014) compare estimated representations to a random network generated from an Erdős-Rényi process (the null model that representations have no systemic structure). Other approaches include permuting the group or diagnostic label of individuals ( Nevado et al, 2021 ), or using additional spatial transformation ( Weakley and Schmitter-Edgecombe, 2014 ) to a put representations in a common coordinate space.…”

Section: Unresolved Methodological Issuesmentioning

confidence: 99%

“…Yet after applying additional spatial transformations to align group representations, they conclude that the two groups in fact have similar representations, despite performing differently on the fluency task. Nevado et al (2021) estimate semantic networks for a group of individuals with Mild Cognitive Impairment using a co-occurrence procedure ( Goñi et al, 2011 ). They find that network measures such as modularity and clustering coefficient are virtually indistinguishable between the Mild Cognitive Impairment and control groups.…”

Section: Estimating Semantic Representations From Fluency Datamentioning

confidence: 99%

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Knowledge Representations Derived From Semantic Fluency Data

Zemla

2022

Front. Psychol.

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The semantic fluency task is commonly used as a measure of one’s ability to retrieve semantic concepts. While performance is typically scored by counting the total number of responses, the ordering of responses can be used to estimate how individuals or groups organize semantic concepts within a category. I provide an overview of this methodology, using Alzheimer’s disease as a case study for how the approach can help advance theoretical questions about the nature of semantic representation. However, many open questions surrounding the validity and reliability of this approach remain unresolved.

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Preserved semantic categorical organization in mild cognitive impairment: A network analysis of verbal fluency

Cited by 11 publications

References 33 publications

Short-term memory, attention, and temporal orientation as predictors of the cognitive impairment in older adults: A cross-sectional observational study

Short-term memory, attention, and temporal orientation as predictors of the cognitive impairment in older adults: A cross-sectional observational study

Prediction of Cognitive Degeneration in Parkinson’s Disease Patients Using a Machine Learning Method

Knowledge Representations Derived From Semantic Fluency Data

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