INS NYC 2024 Program

Symposium	Symposia 3 Program Schedule 02/15/2024 09:00 am - 10:30 am Room: West Side Ballroom - Salon 4 Symposia 3: Current Trends and Future Frontiers in Neuropsychology and Digital Technologies Simposium #3 An Acoustic Analysis of Verbal Serial Learning and Semantic Fluency Test Performance David Libon, Rowan University, Stratrford, United States Rod Swenson, University of North Dakota School of Medicine and Health Sciences, Grand Forks, United States Russell Banks, Linus Health, Boston, United States Daniel Schulman, Linus Health, Boston, United States Sean Tobyne, Linus Health, Boston, United States Connor Higgins Higgins, Linus Health, Boston, United States Jeffrey Pobst, Linus Health, Boston, United States Claudio Toro-Serey, Linus Health, Boston, United States Stephanie Cosentino, Columbia University, New York, United States Catherine Price, University of Florida, Gainesville, United States Category: Dementia (Alzheimer's Disease) Keyword 1: cognitive screening Keyword 2: dementia - Alzheimer's disease Objective: Objective: Digital assessment technology is able to define neurocognitive constructs with behavior previously unobtainable, potentially yielding neurocognitive biomarkers able to identify emergent dementia. Participants and Methods: Methods: A group of community dwelling participants (n=243) were assessed with a 10-minute digitally administered/scored neuropsychological protocol assessing verbal memory (6-word Philadelphia [repeatable] Verbal Learning Test), working memory (backward digit span), and lexical access (‘animal’ fluency) abilities. Cluster analysis classified participants into cognitively normal (CN), dysexecutive mild cognitive impairment (dMCI), and amnestic mild cognitive impairment (aMCI) groups. An acoustic analysis using a uniform corpus of 6 acoustic variables was obtained from the ‘animal’ fluency and P(r)VLT-delay free recall tests. Linear regression analyses of standardized residuals created a single residual acoustic score (RAS) for each test. Results: Results: Logistic regression (CN reference group) using ‘animal’ fluency and P(r)VLT-delay free recall RAS were able to classify participants into their respective groups (p< 0.016, all analyses). Output from dMCI participants from the ‘animal’ fluency test was positive for attenuated voice volume range (p< 0.029); shorter speech duration (p< 0.001); and greater jitter (p< 0.003) than CN participants. The output from aMCI participants from the P(r)VLT delay free recall test also resulted in attenuated voice volume range than other groups (p< 0.016, both analyses); shorter speech duration (p< 0.005) than CN participants; and greater jitter (p< 0.010, both analyses) than other groups. Conclusions: Conclusion: These data suggest that digitally obtained voice behavior might provide neurocognitive biomarkers that are reliable, inexpensive, requiring little time to obtain, that might identify persons at risk for dementia.

Symposium

02/15/2024
09:00 am - 10:30 am
Room: West Side Ballroom - Salon 4

Symposia 3: Current Trends and Future Frontiers in Neuropsychology and Digital Technologies

Simposium #3

An Acoustic Analysis of Verbal Serial Learning and Semantic Fluency Test Performance

David Libon, Rowan University, Stratrford, United States
Rod Swenson, University of North Dakota School of Medicine and Health Sciences, Grand Forks, United States
Russell Banks, Linus Health, Boston, United States
Daniel Schulman, Linus Health, Boston, United States
Sean Tobyne, Linus Health, Boston, United States
Connor Higgins Higgins, Linus Health, Boston, United States
Jeffrey Pobst, Linus Health, Boston, United States
Claudio Toro-Serey, Linus Health, Boston, United States
Stephanie Cosentino, Columbia University, New York, United States
Catherine Price, University of Florida, Gainesville, United States

Category: Dementia (Alzheimer's Disease)

Keyword 1: cognitive screening
Keyword 2: dementia - Alzheimer's disease

Objective:

Objective: Digital assessment technology is able to define neurocognitive constructs with behavior previously unobtainable, potentially yielding neurocognitive biomarkers able to identify emergent dementia.

Participants and Methods:

Methods: A group of community dwelling participants (n=243) were assessed with a 10-minute digitally administered/scored neuropsychological protocol assessing verbal memory (6-word Philadelphia [repeatable] Verbal Learning Test), working memory (backward digit span), and lexical access (‘animal’ fluency) abilities. Cluster analysis classified participants into cognitively normal (CN), dysexecutive mild cognitive impairment (dMCI), and amnestic mild cognitive impairment (aMCI) groups. An acoustic analysis using a uniform corpus of 6 acoustic variables was obtained from the ‘animal’ fluency and P(r)VLT-delay free recall tests. Linear regression analyses of standardized residuals created a single residual acoustic score (RAS) for each test.

Results:

Results: Logistic regression (CN reference group) using ‘animal’ fluency and P(r)VLT-delay free recall RAS were able to classify participants into their respective groups (p< 0.016, all analyses). Output from dMCI participants from the ‘animal’ fluency test was positive for attenuated voice volume range (p< 0.029); shorter speech duration (p< 0.001); and greater jitter (p< 0.003) than CN participants. The output from aMCI participants from the P(r)VLT delay free recall test also resulted in attenuated voice volume range than other groups (p< 0.016, both analyses); shorter speech duration (p< 0.005) than CN participants; and greater jitter (p< 0.010, both analyses) than other groups.

Conclusions:

Conclusion: These data suggest that digitally obtained voice behavior might provide neurocognitive biomarkers that are reliable, inexpensive, requiring little time to obtain, that might identify persons at risk for dementia.