Biology subjects

Olsen, A. S.

Publications and source records attributed to Olsen, A. S..

4 recordsLinked to original sources

bioRxiv2026

Kinetic analysis of CSF to brain tracer exchange in the pig brain under different anesthetic regimes

Introduction: Anesthesia is known to modulate glymphatic clearance and cerebrospinal fluid (CSF) transport in rodents, but how these effects translate to a larger, gyrencephalic brain is unknown. With its anatomical similarity to the human brain, the pig offers a valuable translational model for examining anesthesia-dependent CSF-to-brain transport. Methods: We used dynamic in vivo SPECT/CT imaging for six hours following cisterna magna injection of [99mTc]-DTPA to quantify CSF-to-brain tracer transport in pigs under two anesthesia regimens: ketamine/dexmedetomidine (K/D, n=5) which previously has been shown in rodents to enhance glymphatic influx relative to GABAergic anesthesia, and propofol (PRO, n=5). Brain and CSF spaces were delineated using a data-driven non-negative matrix factorization approach, and tracer kinetics were quantified using a one-tissue compartment model. Results: Brain influx could be stably estimated from 2 hours post-injection. Hierarchical sub-division of the brain parenchyma identified two kinetically distinct components with different anatomical distributions: a surface component, located ventrally and within the interhemispheric fissure, showed faster kinetics than the anatomically deeper and lateral-dorsal component. Consistent with rodent findings, K/D-anesthetized pigs showed 62% (p=0.002) greater brain tracer accumulation than PRO-anesthetized pigs. However, while the brain influx rates did not differ substantially (p=0.047), a 52% higher cumulative CSF tracer concentration (p=0.047) could account for most of the difference by providing greater tracer availability for brain entry. Conclusions: In the larger gyrencephalic pig brain, we found higher brain tracer accumulation under K/D anesthesia compared to PRO anesthesia. A significant portion of this difference is readily explained by higher CSF retention, likely driven by a slower CSF turnover. This underscores the necessity of dynamic CSF tracer concentration measurements when assessing CSF-brain influx, a factor we suggest that future glymphatic studies should take into account.

neuroscience↗

bioRxiv2026

Psilocybin acutely reduces low-frequency BOLD power and frequency-specific connectivity

Psilocybin and other serotonergic drugs acutely alter human brain function and large-scale connectivity as measured with BOLD fMRI, but whether these effects are frequency-specific remains unknown. We applied multitaper spectral and cross-spectral analyses to resting-state fMRI data from 28 healthy volunteers scanned multiple times acutely following oral psilocybin administration (0.2 - 0.3 mg/kg), together with plasma psilocin measurements, to estimate psilocin associations with temporal frequency-specific activity and connectivity. Psilocybin produced a selective reduction in low-frequency spectral power (0.01 - 0.06Hz) and an increase in spectral entropy, with the strongest effects in transmodal networks. We also observed a reduction in low-frequency connectivity energy explained by the unimodal/transmodal axis. These findings demonstrate that psilocin induces spatially distributed, frequency-dependent alterations, suggesting that broadband fMRI analyses may obscure low-frequency dynamics. Frequency-resolved approaches may offer greater sensitivity for characterizing psychedelic effects on brain activity.

neuroscience↗

bioRxiv2024

Uncovering dynamic human brain phase coherence networks

Complex cognitive functions rely on coordinated communication between distributed brain regions, yet capturing these interactions as they evolve over time remains challenging. Traditional analyses of functional brain connectivity largely rely on correlations in signal amplitude, which are sensitive to noise and artifacts such as head motion. Here, we introduce a mixture modeling approach that focuses on the phase of brain signals, allowing dynamic patterns of large-scale synchronization in brain phase coherence networks to be studied directly and in their entirety. We lay the mathematical and conceptual groundwork for phase modeling and introduce the complex angular central Gaussian mixture model, providing a principled way to analyze phase-based interactions across the brain. Applied to fMRI data, the model identifies recurring states of brain-wide synchronized activity that reliably distinguish cognitive tasks and generalize across previously unseen individuals, without requiring any task labels during training. These results show that modeling signal phase offers a clean and informative view of brain synchronization dynamics, opening new avenues for studying large-scale neural coordination. Significance statementUnderstanding how the human brain coordinates activity across distant regions is central to explaining cognition and behavior. Most existing approaches study these interactions by tracking changes in signal strength, which can be strongly affected by non-neural artifacts. Here, we focus instead on the phase relationships between brain signals: How brain regions synchronize their oscillations forming dynamic phase coherence networks. We introduce a flexible and principled mixture modeling framework to capture these patterns directly and reveal recurring states of brain-wide synchronized activity consistent across individuals. This approach uncovers meaningful differences between diverse cognitive tasks, without requiring task labels during training. By emphasizing signal phase rather than amplitude, our method offers a complementary robust and interpretable way to study dynamic brain synchronization patterns.

neuroscience↗

bioRxiv2024

On reconstruction of cortical functional maps using subject-specific geometric and connectome eigenmodes

Understanding the interplay between human brain structure and function is crucial to discern neural dynamics. This study explores the relation between brain structure and macroscale functional activity using subject-specific structural connectome eigenmodes, complementing prior work that focused on group-level models and geometry. Leveraging data from the Human Connectome Project, we assess accuracy in reconstructing various functional MRI-based cortical maps using individualised eigenmodes, specifically, across a range of connectome construction parameters. Our results show only minor differences in performance between surface geometric eigenmodes, a local neighborhood graph, a highly smoothed null model, and individual and group-level connectomes at modest smoothing and density levels. Furthermore, our results suggest that spatially smooth eigenmodes best explain functional data. The absence of improvement of individual connectomes and surface geometry over smoothed null models calls for further methodological innovation to better quantify and understand the degree to which brain structure constrains brain function.

neuroscience↗