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Across the animal kingdom, organisms must navigate a dynamic sensory world to feed, reproduce, and survive. In many species, centralized nervous systems have evolved to become both stereotyped and specialized, enabling animals to contend with and adapt to the specific challenges of their environments. While diffuse, non-specialized networks of neurons are thought to be precursors to centralized brains, it remains unknown how stereotyped regionalization and functional divisions have evolved to support different behavioral needs. The present study addresses these questions with the acoel worm H. miamia whose nervous system is uniquely positioned between diffuse nets and centralized brains. Leveraging a wide array of imaging techniques, single-cell transcriptomics, and behavioral manipulations, the authors present evidence that H. miamia brains lack regionalization and stereotypy, and are instead composed of uniform, repeating units that work in concert to support ethologically relevant foraging behaviors.

This study represents a valuable contribution to the question of how neural circuits evolve for behavior. A key strength is its framing of H. miamia brains as composed of computationally equivalent tiles that can be integrated to enhance foraging—this is an intriguing proposal for how early nervous systems may have coordinated behavior before regional specializations. While anatomical and molecular data provide compelling support for a lack of regionalization, the behavioral manipulations could benefit from more direct demonstrations of the functional uniformity of the tiles and their combined effect on behavior. Some ambiguity also remains regarding how the central network interfaces with sensory and motor regions of the body.

Major Comments

1.     The authors propose that the H. miamia brain consists of “computationally pluripotent” tiles that all access the same information (lines 280-283), as foraging remains largely intact after amputations. However, the anatomical data reveal that tiles often include sensory specializations (line 79) or densities associated with specific organs (e.g., mouth cilia or statocyst innervation), suggesting heterogeneity. Consistent with this, the authors find foraging deficits in mouth-amputated animals, which seems at odds with computational uniformity. Higher resolution anatomical analyses could reveal whether tiles near the mouth receive denser or distinct sensory inputs. If feasible, the EM dataset could be leveraged to trace sensory inputs to tiles. Targeted laser ablations of central tiles that preserve the sensory periphery would also disentangle sensory versus central network contributions. Any of these approaches would help refine the understanding of tiles as repeating computational units and to what extent they require intact sensory inputs to coordinate behavior.

2.     The authors conclude that more tiles enhance “perceptual resolution” (lines 387-88), based on worse foraging deficits the larger the amputation size and with more head segmentations, but these manipulations likely affect other tissues besides the tiles. Larger amputations appear to remove more of the sensory structures, which likely affects food-sensing, and the data do not show whether cutting the head into more pieces disrupts specific commissures or densities near specific organs. Ablations that remove defined numbers of tiles, or correlating the number of remaining tiles post-ablation with foraging across individuals, could provide more direct support than comparing behavior to amputation size, which is an indirect measure of tile number. The observed variability in tile number across wild-type individuals could also be used to directly relate tile counts with foraging. For segmentations, higher-resolution images of the cuts could verify which tissues are sliced. Alternatively, the authors could modify the text to reflect the caveat that effects of tile number and connectivity are being inferred from the manipulations. 

Minor Comments

3.     A reorganization of figures could help to streamline the manuscript and improve conceptual clarity. Figure 1 followed by Figure 3 could first lay the anatomical and molecular description of tile uniformity, while subsequent behavioral analyses in Figure 2 and 4 would provide the functional level and implications of such an organization.

4.     Arena exploration is used as a measure of movement defects (Figure S2d), but because it appears negatively correlated with time spent near food, the interpretation is unclear. For instance, the text states that half-head worms forage normally while mouth-ablated worms do not, with neither showing movement impairments (lines 188-9 and lines 194-96), but Figure S2d shows higher exploration in mouth-ablated worms yet lower exploration in half-head worms. Using a metric independent of food proximity, such as movement in a food-free arena, might better isolate motor effects. A series of video examples from key-point clusters would also illustrate that behavior features look similar across manipulations.  

5.     A schematic of how the central brain is related to the organs, sensory structures, and muscles would help provide a high-level view of how this novel system is organized. Are the cells that make up the 2-layer ring distinct from the sensory cells interspersed in the tiles? Are there differences in cell patches that connect to sensory structures versus those that do not? Where does the central network begin, and where do the sensory and motor periphery and body organs end?

6.     Though right-half amputations do not affect time spent near food (Figure 2e), there may be subtle compensatory changes, given the loss of all right-sided sensory cilia (Figure S1d). Comparing individual trajectories of pose-tracked animals could reveal behavioral biases. Regardless of the results, since most bilaterians use left/right differences to localize food, it would be interesting discussing alternative strategies in the Discussion.  

7.     In the Introduction, an explicit description of the diffuse-to-centralized axis, and how H. miamia differs from other distributed systems, e.g., hydra, jellyfish, or planaria, would provide relevant context for its representations as an intermediate.

8.     Figure S3c: Plotting transition matrices for each condition rather than bar plots may help show the similarities and differences in transitions between conditions.

9.     Figure 2k, l (and lines 268-9): The posture maps for showing effects such as “lack of foraging” or “no turning bias” (line 273) are rather abstract. A matrix of example video clips, e.g., rows representing different clusters and columns representing within-cluster examples, might be more intuitive. A similar visualization would make it easier to appreciate how similar or different a behavior is after a given manipulation. 

10.  Brief descriptions in the text for why each imaging method was used and the choice of markers would improve the interpretability of the experiments for general readers.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.