Favorov, O. V. | bioRxiv Science

bioRxiv2025

Neurodegeneration Associated with Repeated High-Frequency Transcranial Focused Ultrasound

Transcranial Focused Ultrasound (tFUS) is a popular tool for non-invasive neuromodulation which prior testing paradigms have suggested is benign. However, emerging use cases such as clinical therapies and brain-machine interfaces will likely require repeated or long-duration exposures with novel combinations of stimulus parameters (e.g., frequency, focal volume), and the safety of these conditions has not yet been rigorously validated. Therefore, as an initial study we delivered 1.8 MHz tFUS stimulation to the cortex of 4 non-human primates in 4 sessions each of approximately 90 min over 2 weeks. Motor skills were measured daily with a food pellet picking task. Animals were euthanized and the brain sections were processed for histological markers of neurodegeneration (Fluoro-Jade C). While the animals did not show disruption on the behavioral task, there was clear evidence of neurodegeneration in regions associated with short, intermediate, and extended-duration stimulation, but not with unstimulated control tissues in deeper brain areas. Additional neurodegeneration was observed at locations distant from but functionally connected to stimulated regions, consistent with retrograde damage propagated from neuron processes. We discuss alternative causes for the neurodegeneration, and we recommend the use of additional animal studies to understand this phenomenon, especially for novel stimulation paradigms or parameters and applications where extended or repeated exposures are planned.

neuroscience↗

bioRxiv2025

Feedforward Extraction of Behaviorally Significant Information by Neocortical Columns

Neurons throughout the neocortex exhibit selective sensitivity to particular features of sensory input patterns. According to the prevailing views, cortical strategy is to choose features that exhibit predictable relationship to their spatial and/or temporal context. Such contextually predictable features likely make explicit the causal factors operating in the environment and thus they are likely to have perceptual/behavioral utility. The known details of functional architecture of cortical columns suggest that cortical extraction of such features is a modular nonlinear operation, in which the input layer, layer 4, performs initial nonlinear input transform generating proto-features, followed by their linear integration into output features by the basal dendrites of pyramidal cells in the upper layers. Tuning of pyramidal cells to contextually predictable features is guided by the contextual inputs their apical dendrites receive from other cortical columns via long-range horizontal or feedback connections. Our implementation of this strategy in a model of prototypical V1 cortical column, trained on natural images, reveals the presence of a limited number of contextually predictable orthogonal basis features in the image patterns appearing in the columns receptive field. Upper-layer cells generate an overcomplete Hadamard-like representation of these basis features: i.e., each cell carries information about all basis features, but with each basis feature contributing either positively or negatively in the pattern unique to that cell. In tuning selectively to contextually predictable features, upper layers perform selective filtering of the information they receive from layer 4, emphasizing information about orderly aspects of the sensed environment and downplaying local, likely to be insignificant or distracting, information. Altogether, the upper-layer output preserves fine discrimination capabilities while acquiring novel higher-order categorization abilities to cluster together input patterns that are different but, in some way, environmentally related. We find that to be fully effective, our feature tuning operation requires collective participation of cells across 7 minicolumns, together making up a functionally defined 150m diameter "mesocolumn." Similarly to real V1 cortex, 80% of model upper-layer cells acquire complex-cell receptive field properties while 20% acquire simple-cell properties. Overall, the design of the model and its emergent properties are fully consistent with the known properties of cortical organization. Thus, in conclusion, our feature-extracting circuit might capture the core operation performed by cortical columns in their feedforward extraction of perceptually and behaviorally significant information.

neuroscience↗