Speech perception difficulty modulates theta-band encoding of articulatory synergies.

The human brain tracks available speech acoustics and extrapolates missing information such as the speaker's articulatory patterns. However, the extent to which articulatory reconstruction supports speech perception remains unclear. This study explores the relationship between articulatory reconstruction and task difficulty. Participants listened to sentences and performed a speech-rhyming task. Real kinematic data of the speaker's vocal tract were recorded via electromagnetic articulography (EMA) and aligned to corresponding acoustic outputs. We extracted articulatory synergies from the EMA data with principal component analysis (PCA) and employed partial information decomposition (PID) to separate the electroencephalographic (EEG) encoding of acoustic and articulatory features into unique, redundant, and synergistic atoms of information. We median-split sentences into easy (ES) and hard (HS) based on participants' performance and found that greater task difficulty involved greater encoding of unique articulatory information in the theta band. We conclude that fine-grained articulatory reconstruction plays a complementary role in the encoding of speech acoustics, lending further support to the claim that motor processes support speech perception.NEW & NOTEWORTHY Top-down processes originating from the motor system contribute to speech perception through the reconstruction of the speaker's articulatory movement. This study investigates the role of such articulatory simulation under variable task difficulty. We show that more challenging listening tasks lead to increased encoding of articulatory kinematics in the theta band and suggest that, in such situations, fine-grained articulatory reconstruction complements acoustic encoding.

The BRCA1 c.4096+1G>A Is a Founder Variant Which Originated in Ancient Times.

Approximately 30-50% of hereditary breast and ovarian cancer (HBOC) is due to the presence of germline pathogenic variants in the BRCA1 (OMIM 113705) and BRCA2 (OMIM 600185) onco-suppressor genes, which are involved in DNA damage response. Women who carry pathogenic BRCA1 variants are particularly likely to develop breast cancer (BC) and ovarian cancer (OC), with a 45-79 percent and 39-48 percent chance, respectively. The BRCA1 c.4096+1G>A variant has been frequently ascertained in Tuscany, Italy, and it has also been detected in other Italian regions and other countries. Its pathogenetic status has been repeatedly changed from a variant of uncertain significance, to pathogenic, to likely pathogenic. In our study, 48 subjects (38 of whom are carriers) from 27 families were genotyped with the Illumina OncoArray Infinium platform (533,531 SNPs); a 20 Mb region (24.6 cM) around BRCA1, including 4130 SNPs (21 inside BRCA1) was selected for haplotype analysis. We used a phylogenetic method to estimate the time to the most recent common ancestor (MRCA) of BRCA1 c.4096+1G>A founder pathogenic variant. This analysis suggests that the MRCA lived about 155 generations ago-around 3000 years ago.

Prediction of Speech Onset by Micro-Electrocorticography of the Human Brain.

Recent technological advances show the feasibility of offline decoding speech from neuronal signals, paving the way to the development of chronically implanted speech brain computer interfaces (sBCI). Two key steps that still need to be addressed for the online deployment of sBCI are, on the one hand, the definition of relevant design parameters of the recording arrays, on the other hand, the identification of robust physiological markers of the patient's intention to speak, which can be used to online trigger the decoding process. To address these issues, we acutely recorded speech-related signals from the frontal cortex of two human patients undergoing awake neurosurgery for brain tumors using three different micro-electrocorticographic ([Formula: see text]ECoG) devices. First, we observed that, at the smallest investigated pitch (600[Formula: see text][Formula: see text]m), neighboring channels are highly correlated, suggesting that more closely spaced electrodes would provide some redundant information. Second, we trained a classifier to recognize speech-related motor preparation from high-gamma oscillations (70-150[Formula: see text]Hz), demonstrating that these neuronal signals can be used to reliably predict speech onset. Notably, our model generalized both across subjects and recording devices showing the robustness of its performance. These findings provide crucial information for the design of future online sBCI.