Automated speech and language analysis

1 paper4 findings

C, W, M, H: cells, worms or flies, mice, humans. Dark: independent labs found the same change there. Light: one lab or one study. Outline: nothing yet.

Labs are independent when their papers share no last author and no institution.

Against: findings that point the other way from most findings on the same outcome.

On aging: the drug’s trials ARN judged to be about aging, of every trial that tests it for any use.

No results posted: trials that ended more than a year ago with no results on ClinicalTrials.gov and no paper in ARN’s record.

Company ties: findings with an author at a company, or from a paper whose competing-interests statement declares ties to one.

1 lab in 4 countries

Humans1

Associations

Gray matter volume

upin humans1

1 study

Automated speech and language analysis predicts gray matter volume in humans (r = .34, p = .006).
“as well as for normalized GM volume ( r = .34, p = .006).”
Humanscontrolling for age, sex, education, and MoCA scoresword-property features, combined fluency tasksn = 150
Predicting brain health in older adults through automated language measures
AAIC 2025 abstract25 Dec 2025

Latest

1 in 12 months

AAIC 2025 Abstract25 Dec

Predicting brain health in older adults through automated language measures

Cross-sectional study of 150 peopleBiomarkers

Paper

Automated speech and language analysis

Associations

Latest

Predicting brain health in older adults through automated language measures

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