Limitations of multiexponential T1 mapping of cortical myeloarchitecture

28 Nov

The Pattern Glare Test: Further Exploration of Methodological and Demographic Factors

Purpose: The Pattern Glare Test (PGT) is a widely used tool for assessing visual discomfort and perceptual distortions elicited by repetitive, high-contrast patterns. It has been applied across diverse populations and research fields, including neuroscience, cognitive psychology, optometry, orthoptics, and ophthalmology. Despite its broad use, considerable variation exists in how the PGT is implemented, particularly regarding stimulus parameters, participant characteristics, and scoring methods. Such variability introduces potential confounding factors that may obscure the psychological and neurological mechanisms underlying pattern-induced visual discomfort.

Methods: We analyzed PGT data from 184 neurotypical young adults (72 males and 112 female; mean age = 24.29 years, SD = 4.75) to examine methodological influences on reports of associated visual distortions (AVDs) and discomfort. Our main analyses utilized cumulative link mixed models and cluster analysis of intraindividual responses.

Results: Significant effects of trial progression and sex (biological; male/female) on subjective responses have been revealed, with the effect of sex not accounted for by migraine occurrence. Substantial overlap among color-related AVDs supported their consolidation into a single category. 

19 Nov

Digital speech biomarkers for assessing cognitive decline across neurodegenerative conditions

This study investigates speech impairments in individuals with mild cognitive impairment due to Alzheimer's disease (MCI-AD), mild cognitive impairment with Lewy bodies (MCI-LB), and Parkinson's disease with mild cognitive impairment (PD-MCI), compared to healthy controls (HC), aiming to identify linguistic and acoustic digital biomarkers that differentiate these groups. Monologue recordings were collected from 68 HC, 42 MCI-AD, 50 MCI-LB, and 47 PD-MCI participants (ON state). Participants were instructed to speak spontaneously for one and a half minutes. Speech was automatically transcribed, manually corrected, and analyzed using natural language processing to extract eight linguistic (lexical/syntactic) and four acoustic (prosodic) biomarkers. Group differences were assessed using the Mann-Whitney U test, with Spearman's correlation used to examine associations with clinical and MRI measures (FDR-corrected). Machine learning models (XGBoost) were applied to evaluate the classificatory and predictive potential of speech features. Distinct speech patterns were observed across groups: MCI-AD participants exhibited reduced use of function words, resulting in increased content density, PD-MCI participants used shorter sentences and fewer coordinating conjunctions with longer pauses, and MCI-LB participants exhibited greater lexical repetition than MCI-AD.