Age-specific templates improve the quality of functional alignment using hyperalignment algorithms, and the improvement is consistent across age groups, validation analyses, and datasets.

We modeled shared representational spaces in macaque brains using fMRI data from 12 rhesus monkeys obtained during movie-viewing and response hyperalignment (RHA). RHA greatly improved inter-subject c...

Functional connectivity in network neuroscience is traditionally characterized using time-averaged correlations between brain regions. While these summaries capture stable large-scale organization, they do not fully reflect the temporal structure of moment-to-moment interactions. Here, we investigate how the order of interaction used to represent brain dynamics shapes the organization recovered from neural data. We compare three interaction representations of fMRI dynamics: regional activation (node time series), pairwise co-fluctuations (edge time series), and higher-order triplet interactions (triangle time series); within a common topological framework using Mapper from topological data analysis (TDA). Across task and resting-state data, Mapper representations derived from pairwise co-fluctuations more distinctly segregate task conditions than activation-based or higher-order representations. This organization reflects structured coordination patterns beyond activation polarity and is driven by high-amplitude interaction events. Beyond task states, modularity quality computed across all Mapper representations is highest for edge time series and selectively associated with stable individual differences: higher modularity relates to higher conscientiousness and lower internalizing and externalizing symptom dimensions. Together, these findings suggest that behaviorally relevant information is reflected in the topology of moment-to-moment brain interactions. Topological analysis of interaction-level dynamics therefore provides a complementary and interpretable framework for linking large-scale neural coordination to cognition, personality, and mental health. ### Competing Interest Statement The authors have declared no competing interest. National Institute of Mental Health, https://ror.org/04xeg9z08, MH127608 Stanford Maternal and Child Health Research Institute, https://ror.org/00yt0ea73, Faculty Scholar Award

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A recent study introduces a neural code conversion method that aligns brain activity across individuals without shared stimuli, using deep neural network-derived features to match stimulus content.

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Onavg is a surface template of the human cortex. In contrast to existing templates, the cortical surface is uniformly sampled, which has advantages in numerous applications.

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The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that under...

Artificial Neural networks (ANN) trained on complex tasks are increasingly used in neuroscience to m