Disrupted Brain Activation and Deactivation Pattern during Semantic Verbal Fluency Task in Patients with Major Depression

2016 ◽  
Vol 74 (2) ◽  
pp. 69-77 ◽  
Author(s):  
Masahiro Takamura ◽  
Yasumasa Okamoto ◽  
Go Okada ◽  
Shigeru Toki ◽  
Tetsuya Yamamoto ◽  
...  
Keyword(s):  
Major Depression ◽  
Verbal Fluency ◽  
Brain Activation ◽  
Verbal Fluency Task ◽  
Semantic Verbal Fluency ◽  
Fluency Task

Temporal Analysis of the Semantic Verbal Fluency Task in Persons with Subjective and Mild Cognitive Impairment

2019 ◽  
Author(s):  
Nicklas Linz ◽  
Kristina Lundholm Fors ◽  
Hali Lindsay ◽  
Marie Eckerström ◽  
Jan Alexandersson ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Verbal Fluency ◽  
Temporal Analysis ◽  
Verbal Fluency Task ◽  
Semantic Verbal Fluency ◽  
Fluency Task

scholarly journals Changes in Cortical Activation Patterns in Language Areas following an Aerobic Exercise Intervention in Older Adults

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Joe Nocera ◽  
Bruce Crosson ◽  
Kevin Mammino ◽  
Keith M. McGregor
Keyword(s):  
Older Adults ◽  
Aerobic Exercise ◽  
Verbal Fluency ◽  
Exercise Intervention ◽  
Community Dwelling ◽  
Spin Group ◽  
Verbal Fluency Task ◽  
Semantic Verbal Fluency ◽  
Link Type ◽  
Fluency Task

Previous work has shown that older adults who evidence increased right inferior frontal gyrus (IFG) activity during language tasks show decreased sematic verbal fluency performance. The current study sought to evaluate if an aerobic exercise intervention can alter patterns of brain activity during a semantic verbal fluency task assessed by functional magnetic resonance imaging (fMRI). Thirty-two community-dwelling, sedentary older adults were enrolled to a 12-week aerobic “Spin” exercise group or a 12-week nonaerobic exercise control condition (Balance). Thirty participants completed their assigned intervention (16 Spin; 14 Balance) with pre- and postintervention assessments of a semantic verbal fluency task during fMRI and estimated VO2max testing. There was a significant increase in the change scores for estimated VO2max of the Spin group when compared to the Balance group. Semantic verbal fluency output within the scanner was also improved in the Spin group as compared to controls at postassessment. Group fMRI comparisons of IFG activity showed lower activity in the right IFG following the intervention in the aerobic Spin group when compared to the Balance group. Regression analysis of imaging data with change in both estimated VO2max and semantic verbal fluency was negatively correlated with activity in right IFG. The current work is registered as clinical trial with NCT01787292 and NCT02787655.

scholarly journals Clinical use of deep speech parameters derived from the semantic verbal fluency task

2021 ◽  
Vol 17 (S8) ◽  
Author(s):  
Daphne BG ter Huurne ◽  
Inez HGB Ramakers ◽  
Nicklas Linz ◽  
Alexandra König ◽  
Kai Langel ◽  
...  
Keyword(s):  
Verbal Fluency ◽  
Clinical Use ◽  
Verbal Fluency Task ◽  
Semantic Verbal Fluency ◽  
Fluency Task

scholarly journals Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task

2018 ◽  
Vol 45 (3-4) ◽  
pp. 198-209 ◽  
Author(s):  
Alexandra König ◽  
Nicklas Linz ◽  
Johannes Tröger ◽  
Maria Wolters ◽  
Jan Alexandersson ◽  
...  
Keyword(s):  
Task Performance ◽  
Verbal Fluency ◽  
Area Under Curve ◽  
Semantic Category ◽  
Verbal Fluency Task ◽  
Semantic Verbal Fluency ◽  
Fine Grained ◽  
Fully Automatic ◽  
Highly Correlated ◽  
Fluency Task

Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Methods: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer’s or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Results: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). Conclusion: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

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