In primary progressive aphasia, differential diagnosis between non-fluent (nfvPPA) and logopenic (lvPPA) variants can be clinically challenging. Although linguistic features from connected speech may aid in PPA subtype classification, they depend on labor-intensive transcriptions. In contrast, acoustic features are extracted directly from the audio signal and have shown potential in capturing variant-specific patterns, but their diagnostic relevance is not well understood.In this study, we analyzed connected speech recordings from 242 individuals (40 healthy controls, 91 lvPPA, 111 nfvPPA) performing a ∼2-min picture description task. We extracted 103 acoustic features from each recording and reduced them to 9, which we finally grouped into four domain scores representing speech rate and voice quality. Domain scores were tested as predictors in classification models to detect PPA and distinguish between PPA variants. Voxel-based morphometry was used to investigate brain-behavior relationships.Classification models combining the four domain scores and MMSE yielded 88% balanced-sample accuracy for PPA-versus-controls and 73% accuracy for lvPPA-versus-nfvPPA. Pitch and speech rate were the most discriminative markers. Voxel-based morphometry associated speech rate with the frontal speech production network, pitch with the middle frontal and inferior frontal gyrus, perturbation with the insula, and spectrum with the basal ganglia and the thalamus.We showed that acoustic analysis of brief speech samples has the potential to aid the diagnostic process and contribute to more nuanced phenotyping. With further validation, such measures could be integrated into clinical workflows as low-burden, objective tools to support differential diagnosis and track disease progression in PPA.
The acquisition and consolidation of arbitrary visuomotor mappings (AVMMs) engage premotor regions, yet the distinct contributions of the dorsal (PMd) and ventral (PMv) premotor cortices remain unclear. Here, we applied continuous theta-burst stimulation (cTBS) immediately before AVMM learning to transiently disrupt PMd, PMv, or an occipital control site (V1) in 19 healthy young adults, using a within-subjects design. Participants learned arbitrary stimulus–response associations through trial-and-error feedback and completed a 24-h delayed recall test. Trial-by-trial accuracy and response times were modeled using state-space and Rescorla–Wagner frameworks to quantify learning dynamics, prediction errors, and stabilization across days. Disruption of both PMd and PMv slowed early acquisition of AVMMs to a comparable extent relative to V1. However, only PMd disruption additionally impaired next-day recall, rendering recall performance dependent on sleep quality and decoupled from learning-day performance, whereas recall following PMv disruption remained comparable to the control condition. These findings indicate that PMd, but not PMv, contributes to the stabilization of newly learned visuomotor associations, providing causal, stage-specific evidence for a functional dissociation within the human premotor cortex: PMd and PMv support early rule formation and exploratory learning, whereas only PMd contributes to subsequent stabilization of newly learned visuomotor associations. Furthermore, they emphasize the modulatory influence of sleep on memory retention when consolidation processes are compromised, advancing mechanistic models of premotor contributions to associative sensorimotor learning.
