Reply to ‘Language is widely distributed throughout the brain’
Related Articles
A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals across 100 h of speech production and comprehension as participants engaged in open-ended real-life conversations. We extracted low-level acoustic, mid-level speech and contextual word embeddings from a multimodal speech-to-text model (Whisper). We developed encoding models that linearly map these embeddings onto brain activity during speech production and comprehension. Remarkably, this model accurately predicts neural activity at each level of the language processing hierarchy across hours of new conversations not used in training the model. The internal processing hierarchy in the model is aligned with the cortical hierarchy for speech and language processing, where sensory and motor regions better align with the model’s speech embeddings, and higher-level language areas better align with the model’s language embeddings. The Whisper model captures the temporal sequence of language-to-speech encoding before word articulation (speech production) and speech-to-language encoding post articulation (speech comprehension). The embeddings learned by this model outperform symbolic models in capturing neural activity supporting natural speech and language. These findings support a paradigm shift towards unified computational models that capture the entire processing hierarchy for speech comprehension and production in real-world conversations.
Language measures correlate with other measures used to study emotion
Researchers are increasingly using language measures to study emotion, yet less is known about whether language relates to other measures often used to study emotion. Building on previous work which focuses on associations between language and self-report, we test associations between language and a broader range of measures (self-report, observer report, facial cues, vocal cues). Furthermore, we examine associations across different dictionaries (LIWC-22, NRC, Lexical Suite, ANEW, VADER) used to estimate valence (i.e., positive versus negative emotion) or discrete emotions (i.e., anger, fear, sadness) in language. Associations were tested in three large, multimodal datasets (Ns = 193–1856; average word count = 316.7–2782.8). Language consistently related to observer report and consistently related to self-report in two of the three datasets. Statistically significant associations between language and facial cues emerged for language measures of valence but not for language measures of discrete emotions. Language did not consistently show significant associations with vocal cues. Results did not tend to significantly vary across dictionaries. The current research suggests that language measures (in particular, language measures of valence) are correlated with a range of other measures used to study emotion. Therefore, researchers may wish to use language to study emotion when other measures are unavailable or impractical for their research question.
Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease
Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies.
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
Baicalein inhibits cell proliferation and induces apoptosis in brain glioma cells by downregulating the LGR4-EGFR pathway
Patients diagnosed with brain glioma have a poor prognosis and limited therapeutic options. LGR4 is overexpressed in brain glioma and involved in the tumorigenesis of many tumors. Baicalein (BAI) is a kind of flavonoid that has exhibited anti-tumor effects in various tumors. Nevertheless, the functions and associations of BAI and LGR4 in brain glioma remain unclear. In this study, Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases were used to perform expression and survival analysis of LGR4 in brain glioma patients. Subsequently, the significance of LGR4-EGFR in brain glioma cells (HS683 and KNS89) and brain glioma animal models was explored by RNA interference and subcutaneous transplantation. Additionally, brain glioma cells were treated with BAI to explore the roles and mechanisms of BAI in brain glioma. The results showed that LGR4 was highly expressed in brain glioma and was related to a poor prognosis. LGR4 knockdown repressed the proliferation and EGFR phosphorylation but induced apoptosis in brain glioma cells. However, these effects were reversed by EGFR overexpression and CBL knockdown. In contrast, both in vitro and in vivo experiments revealed that LGR4 overexpression facilitated brain glioma cell malignant behavior and promoted tumor development, but these effects were rescued by BAI and an EGFR inhibitor. Furthermore, si-LGR4 accelerated EGFR protein degradation, while oe-LGR4 exhibited the opposite effect. Without affecting normal cellular viability, BAI inhibited malignant behavior, interacted with LGR4, and blocked the LGR4-EGFR pathway for brain glioma cells. In conclusion, our data suggested that BAI inhibited brain glioma cell proliferation and induced apoptosis by downregulating the LGR4-EGFR pathway, which provides a novel strategy and potential therapeutic targets to treat brain glioma.
Responses