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Ortega Caro, J.

Publications and source records attributed to Ortega Caro, J..

2 recordsLinked to original sources

Selective changes in cortical cholinergic signaling during learning

Cognition relies on the function of local and long-range neural circuits in the neocortex, which are dynamically regulated by neuromodulatory signals including acetylcholine (ACh). Recent work suggests that the role of acetylcholine varies across cortical areas and behavioral states. In addition, the precise role of cholinergic signaling during learning and plasticity remains unclear. We performed simultaneous, dual-color mesoscopic imaging of ACh and calcium signaling in the neocortex of awake mice across multiple stages of visual task learning to identify learning-related plasticity in neural activity and cholinergic signaling. Using a novel Pattern Reconstruction and Interpretation with a Structured Multimodal transformer (PRISMT) model combining masked autoencoding with multimodal causal attention, we identified key cortical regions that reorganize with learning. We found that cholinergic signaling exhibited spatiotemporally selective plasticity in frontal cortical subnetworks during visual perceptual learning. Overall, our findings demonstrate the utility of transformer models for generating biologically interpretable insight into large multimodal datasets and link long-term functional reorganization of cortical networks to enhanced task performance.

neuroscience↗

BrainLM: A foundation model for brain activity recordings

AO_SCPLOWBSTRACTC_SCPLOWWe introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research.

neuroscience↗