New simulation framework for transcranial focused ultrasound… Novel voltage imaging in awake, behaving mice… Cortical dynamics impact perceptual decision making… Internal states shape behavioral decisions…
Rapid Simulation Framework Predicts Focal Spot Characteristics For Transcranial Focused Ultrasound
Transcranial focused ultrasound (FUS) is a non-invasive technique used to induce brain tissue changes in humans. Successful application of FUS requires delivery of ultrasound beams at an indicated position and intensity through the skull. However, patient skulls vary widely in terms of size, thickness, shape, and bone composition, and these variations impact the intensity and position of ultrasound waves delivered during FUS. Focal spot characterization, which involves measuring the temperature rise during treatment, ensures targeted and safe FUS. However, current methods used to characterize focal spots rely on a tedious calibration process at the start of treatment, often resulting in the use of higher energies than needed and unwanted heating in the brain. To develop an alternative, faster approach, Dr. Kim Butts Pauly and colleagues at Stanford University developed a rapid 3D numeric simulation framework to predict focal spot characteristics before ultrasound delivery. The researchers’ framework utilizes patient-specific skull models along with a novel hybrid angular spectrum (HAS) method to simulate the delivered ultrasound. To produce a treatment-specific simulation of focal spots, the researchers accounted for both patient position and sonication-specific parameters. Dr. Pauly and her team evaluated the framework’s accuracy by comparing the predicted focal spots to the data of patients from an essential tremor clinical study. The simulation framework accurately predicted the focal spot position. Once patient-specific information, as well as skull and brain-tissue properties, were factored into the model, the framework also correctly predicted focal spot temperature rise. Preliminary data also demonstrated the framework’s could also predict focal spot shape. Due to its computational speed, the HAS simulation framework shows promise in performing real-time FUS simulation or rapidly modifying treatment parameters and could help limit the sonication number, shorten treatment time, and improve efficacy and safety. The framework offers potential as a clinical tool for patient screening and treatment design and may be useful for other FUS applications, such as blood-brain barrier opening or neuromodulation.
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Novel Genetically Encoded Voltage Indicator Enables Imaging of Neural Activity in Awake, Behaving Mice
Monitoring the electrical membrane voltage of individual neurons in awake, behaving animals is a central objective of much neuroscience research. In the past decade, the development of genetically encoded voltage indicators (GEVIs), such as those derived from rhodopsins or green fluorescent proteins, has advanced the neural imaging field. However, existing indicators in living brains are hindered by their low signal-to-noise ratio (SNR), low stability, and weak localization within the tissue. Dr. Ed Boyden, Dr. Xue Han, and colleagues at MIT and Boston University report the development of SomArchon, a GEVI with increased temporal precision, sensitivity, and stability compared to prior published reagents. To generate SomArchon, the authors utilized their recently developed GEVI, Archon1, and conducted a screen for a peptide to localize the indicator to the soma. SomArchon’s improved SNR enabled the imaging of multiple brain regions in awake, behaving mice, including the cortex, hippocampus and striatum, while using easily accessible one-photon microscopy. SomArchon detected both positive and negative changes in striatal neural activity during mice movement, enabling the detection of intracellular subthreshold oscillations within the hippocampus. Compared to existing indicators, SomArchon achieved a several-fold increase in the number of neurons that could be imaged, thereby allowing the analysis of 13 neurons simultaneously. SomArchon holds promise for facilitating expanded neuronal imaging across a range of applications.
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Task-Dependent Variables Impact The Number and Dynamics of Participating Cortical Regions
Decision making in response to sensory stimuli is a vital cognitive skill essential for survival. However, the neural pathways underlying such complex perceptual decisions and how they are adapted during different tasks are not well understood. Experiments involving the inactivation of specific cortical regions in rodents suggest that relatively few brain areas are required for perceptual decision making. However, some researchers argue that the specific cognitive requirements of a task impact the extent to which various brain areas are engaged. To investigate this theory, Dr. Carlos Brody and colleagues from Princeton University monitored the activity profiles across the full dorsal cortex of mice to investigate the mechanisms underlying decision making in response to stimuli. Within a virtual reality environment, mice navigated three related tasks with various cognitive requirements: an evidence-accumulation task, a memory-guided task, and a visually guided task. By measuring calcium activity patterns and behavioral effects, the authors found that extensive dorsal cortical areas contributed to execution of the memory and evidence-dependent tasks, but not the visually guided task. In addition, different levels of cognitive computations were linked to different behavioral dynamics. Using a recurrent neural network model, the authors concluded that even when sensory stimuli and motor output variables are held constant, tasks with more complex cognitive demands engage greater computational diversity across the cortex. Together, these findings contribute to the understanding of mechanisms underlying perceptual decision-making.
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Internal States Modulate Sensory Processing and Behavior Patterns in Drosophila melanogaster
Internal biological states, such as needs and desires, drive behavioral decisions and sensory processing. However, current research methods are unable to distinguish or measure shifts in internal states over time. To address this unmet need, Dr. Mala Murthy and colleagues at Princeton University employed multiple predictive models to investigate the internal states that underlie behavioral decisions of the fly Drosophila melanogaster. The authors used both hidden state models, which attempt to determine whether some underlying state explains an animal’s behavior, and sensorimotor models, which utilize a filter that defines how a given sensory input is linked to future actions. The authors observed acoustic behaviors during courtship, as D. melanogaster males shape their song performances using feedback signals from their partner. By studying these acoustic patterns, the authors found that male flies employed three distinctive sensorimotor strategies, with each strategy corresponding to a unique relationship between input feedback and resulting output behaviors (e.g., songs produced during courtship with a partner). The authors also discovered that a specific pair of neurons previously believed to function in the song motor pathway were also involved in this behavior-modifying feedback processing. Taken together, this new model provides valuable insight into how internal states modulate environmental sensory processing and resulting behavior patterns.