The brain’s action-mode network
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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.
Advancements in ultrafast photonics: confluence of nonlinear optics and intelligent strategies
Automatic mode-locking techniques, the integration of intelligent technologies with nonlinear optics offers the promise of on-demand intelligent control, potentially overcoming the inherent limitations of traditional ultrafast pulse generation that have predominantly suffered from the instability and suboptimality of open-loop manual tuning. The advancements in intelligent algorithm-driven automatic mode-locking techniques primarily are explored in this review, which also revisits the fundamental principles of nonlinear optical absorption, and examines the evolution and categorization of conventional mode-locking techniques. The convergence of ultrafast pulse nonlinear interactions with intelligent technologies has intricately expanded the scope of ultrafast photonics, unveiling considerable potential for innovation and catalyzing new waves of research breakthroughs in ultrafast photonics and nonlinear optics characters.
Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity
Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, the brain dynamics underlying this relationship are poorly understood. Here, to address this issue, we analyzed multimodal data from female participants in the Adolescent Brain and Cognitive Development (longitudinal, N = 441; aged 9–12 years) and Human Connectome-Aging (cross-sectional, N = 130; aged 36–60 years) studies. Age-specific intrinsic functional brain network dynamics mediated the link between reproductive aging and perceptions of greater interpersonal adversity. The adolescent profile overlapped areas of greater glutamatergic and dopaminergic receptor density, and the middle-aged profile was concentrated in visual, attentional and default mode networks. The two profiles showed opposite relationships with patterns of functional neural network variability and cortical atrophy observed in psychosis versus major depressive disorder. Our findings underscore the divergent patterns of brain aging linked to reproductive maturation versus senescence, which may explain developmentally specific vulnerabilities to distinct disorders.
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