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Brains evolved to perform sensorimotor transformations, enabling animals to adapt to changing environments and giving rise over time to increasingly specialized and stereotyped regions. In mammals, for example, feeding depends on distinct neural circuits for smell, taste, chewing, and internal-state integration. How and why such specialized architectures first emerged, however, remains a fundamental question.

The authors address this question in the acoel worm Hofstenia miamia, a lineage positioned between animals with diffuse nerve nets and those with regionalized brains. Using imaging, single-cell RNA-seq analyses, and targeted perturbations during foraging, they propose that the H. miamia brain comprises repeated computational modules capable of carrying out the sensorimotor operations required for foraging. This modular organization represents a compelling potential evolutionary intermediate from which more complex, specialized regions could arise.

This evolutionary framing is a major strength. By identifying putative ancestral building blocks of sensorimotor computation, the study provides a powerful framework for thinking about how specialized brain regions may have evolved. At the same time, the manuscript would benefit from a more explicit conceptual model describing how repeated units integrate sensory cues and drive motor programs.

Major comments:

1. The authors argue that the H. miamia brain consists of independent units capable of performing sensorimotor transformations during foraging, based on preserved prey-approach behavior after removal of different brain regions (Fig. 2e–g). However, the deficits observed following mouth removal (Fig. 2e), which likely reflects disruption of key sensory inputs, complicate the claim of full computational independence. The mouth experiments, together with the anatomical data (Fig. 2e, S1), point to specialized peripheral structures, such as the frontal organ, statocyst, and pharyngeal network, whose connectivity appears essential for effective sensorimotor transformations.

Clarifying in the text that the proposed computational units depend on specialized peripheral sensory and/or motor elements would better align the interpretation with the data. Such a clarification would not undermine the concept of repeated sensorimotor units in the central brain, but rather refine the model by emphasizing that their computational competence is conditional on intact peripheral inputs. If feasible within the scope of the existing EM dataset, tracing connections between peripheral sensory and motor structures and individual brain units could further test whether all units are equally equipped to support foraging behavior, while also highlighting the exceptional richness of the presented anatomical data.

Minor comments:

2. The behavioral impairment observed only in the six-fragment condition (Fig. 4f) is difficult to interpret. It is unclear why six-fragment cuts disrupt foraging whereas two- or four-fragment cuts do not. One possibility is that essential pathways to peripheral structures, such as the frontal organ, are severed only at higher fragmentation levels. If possible, the above EM tracings could provide anatomical insights. Alternatively, explicitly discussing such testable hypotheses would help clarify the interpretation of this result.

3. The authors emphasize the lack of stereotypy in H. miamia brains, showing that cluster number increases with age but varies widely among same-age individuals (Fig. 1l). This variability is important for interpreting the foraging assays and offers an opportunity to further test the idea that an increased number of cell clusters supports foraging efficiency. Because removing the same anatomical region likely eliminates different numbers of clusters across animals, quantifying cluster counts removed in each manipulation and testing whether animals with fewer initial clusters show stronger deficits would clarify how cluster number relates to behavioral impact.

4. The existing variability also enables a non-manipulative test: correlating natural variation in cluster number with foraging efficiency, within and across age groups, could strengthen the proposed link between computational unit number and behavior. In addition, the reported dorso–ventral asymmetry, with the ventral half containing fewer edges (L. 84), suggests a testable prediction. Comparing dorsal versus ventral amputations could provide further support for the model.

5. The sex of H. miamia animals is not mentioned. Given the complex hermaphroditic biology of this species, anatomical or behavioral differences among males, females, or sequential hermaphrodites, such as the number of cell clusters, could influence the results and interpretation. If such data are available, including them would strengthen the manuscript. Alternatively, the sex or reproductive state of the experimental animals should be specified in the Methods, and the potential impact of sex differences briefly discussed.

6. Bilaterians, including H. miamia, may localize prey by comparing sensory input across left and right sides. A directed analysis of the hunting assay (Fig. 4c) could clarify this. Does removal of brain tissue cause a tracking deficit specifically on the corresponding side? Such an analysis would help refine the computational model: the absence of lateralized deficits may support the independence of units, whereas side-specific deficits may reveal interactions among units.

7. The authors employ a broad variety of methods to characterize the H. miamia nervous system. At times, it may be difficult for a general audience to understand why specific methods or markers were chosen. For example, the rationale for using voltage dye to visualize the neuropil (Fig. 1c) or for selecting Par3 or pERK as neuronal markers (Fig. 1e,f) is not fully explained. Are these markers validated in H. miamia, or inferred from related systems? The EM dataset may provide additional evidence (e.g., synaptic boutons) to support neuronal identity. Including brief explanations of marker selection and validation, either in the main text or the Methods, would improve clarity.

8. The authors describe the repetitive nature of the nervous system using varied terminology, such as “regions,” “cell clusters,” “edges,” and “tiles.” Although these terms appear to refer to distinct units, their relationships remain unclear. A schematic illustrating the anatomical units, their constituent cell types (including non-neuronal types), and their connectivity would help readers understand the organization of the system. Such a schematic could also serve as a simplified computational model and aid comparison with cnidarian diffuse nets and centralized bilaterian brains (Fig. 1a).

9. The term “pluripotency” is commonly used to describe stem cells with broad differentiation potential. To avoid confusion, an alternative term, such as “replica” or “repeated computational unit”, may more accurately communicate the intended meaning.

10. In Fig. 2l, the amputation conditions used to generate postural cluster occupancy plots are not clearly described. In addition, legends overlap with plotted data in several figures (Figs. 4a,f; S2m,n); moving legends outside the plotting area would improve readability.

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.