Brain waves in both actual and imagined navigation show structured oscillations
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Human neural dynamics of real-world and imagined navigation
The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and envisioning of the future. While rodent studies suggest the medial temporal lobe, especially the hippocampus, is involved in these functions, its role in human imagination remains uncertain. In human participants, imaginations can be explicitly instructed and reported. Here we investigate hippocampal theta oscillations during real-world and imagined navigation using motion capture and intracranial electroencephalographic recordings from individuals with chronically implanted medial temporal lobe electrodes. Our results revealed intermittent theta dynamics, particularly within the hippocampus, encoding spatial information and partitioning navigational routes into linear segments during real-world navigation. During imagined navigation, theta dynamics exhibited similar patterns despite the absence of external cues. A statistical model successfully reconstructed real-world and imagined positions, providing insights into the neural mechanisms underlying human navigation and imagination, with implications for understanding memory in real-world settings.
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.
Catalytic dwell oscillations complete the F1-ATPase mechanism
The F1-ATPase molecular motor rotates subunit-γ in 120° power strokes within its ring of three catalytic sites separated by catalytic dwells for ATP hydrolysis and Pi release. By monitoring rotary position of subunit-γ in E. coli F1 every 5 μs, we resolved Stage-1 catalytic dwell oscillations that extend from -13° to 13° centered at 0° consistent with F1 structures containing transition state inhibitors, which decay by a first order process consistent with ATP hydrolysis. During Stage-2, 80% of the oscillations extend from 3° and 25° centered at 14°, while 20% are centered at 33° and can extend to 27°–44° comparable to the ATP binding position. Remarkably, in Stage-3 subunit-γ returns to 0° to end the catalytic dwell, which keeps the start of power strokes in phase for consecutive rotational events. These newly observed states fit with F1 structures that were inconsistent with the canonical mechanism, and indicate that catalytic dwell oscillations must persist until the correct occupancy of substrates and products occurs at all three catalytic sites. When that condition is met, F1 can proceed to the next power stroke. Understanding the basis of these catalytic dwell oscillations completes the F1-ATPase rotary mechanism.
Collective chemo-mechanical oscillations and cluster waves in communicating colloids
Communication and feedback are crucial for the self-organization and the emergent viscoelastic behavior of life-like soft matter systems. However, the specific effects of communication between the individual components on their properties, interactions, and collective dynamics are not fully understood. Here, we report on two-dimensional Brownian dynamics simulations of catalytically active, non-motile hydrogel colloids with explicit resolution of chemical signaling clouds and chemo-mechanical feedback through a size-dependent permeability for the fuel. In particular, we investigate how their spatiotemporal structure and dynamical behavior depend on the communication magnitude and the colloid density. We discover a diverse range of nonequilibrium structures and active phases, including transitions from uncorrelated to synchronized oscillations and the emergence of elastic cluster waves for increasing chemo-mechanical coupling. Our findings highlight microscopic physical principles behind communication-driven cooperativity and could inform the design of active soft matter systems with adaptive functionalities.
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.
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