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This research applies hybrid deep learning architectures, specifically combining CNNs and Vision Transformers, to model ADHD dynamics as stochastic processes for optimizing methylphenidate intervention policies. By bridging theoretical nonlinear control models with empirical fMRI data, the study demonstrates a direct application of machine learning to complex biological systems rather than focusing on consumer or general software topics.
A discrete-continuous-time stochastic framework for attention-deficit/hyperactivity disorder-related symptom modeling and pharmacological intervention via methylphenidate
arXiv:2610.25285v1 Announce Type: new Abstract: This paper presents an original mathematical and computational framework for modeling the efficacy of methylphenidate as an optimal control policy acting on the nonlinear dynamics of attention deficit hyperactivity disorder. Neural processes in ADHD are conceptualized as stochastic processes with trajectories that inherently converge towards stable, yet dysfunctional patterns associated with clinical symptoms. The pharmacological intervention is mathematically formalized as an external control signal that reassesses the system's drift and redirects these trajectories towards neurotypical regimes. To bridge these theoretical stochastic models with empirical clinical data, latent representations are inferred using a novel deterministic hybrid deep learning architecture, combining a one-dimensional convolutional neural network and a Vision Transformer, trained on preprocessed functional magnetic resonance imaging data.
Abstract:This paper presents an original mathematical and computational framework for modeling the efficacy of methylphenidate as an optimal control policy acting on the nonlinear dynamics of attention deficit hyperactivity disorder. Neural processes in ADHD are conceptualized as stochastic processes with trajectories that inherently converge towards stable, yet dysfunctional patterns associated with clinical symptoms. The pharmacological intervention is mathematically formalized as an external control signal that reassesses the system's drift and redirects these trajectories towards neurotypical regimes. To bridge these theoretical stochastic models with empirical clinical data, latent representations are inferred using a novel deterministic hybrid deep learning architecture, combining a one-dimensional convolutional neural network and a Vision Transformer, trained on preprocessed functional magnetic resonance imaging data.
Machine Learning (cs.LG); Probability (math.PR)
arXiv:2610.25285 [cs.LG]
(or arXiv:2610.25285v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.25285 Focus to learn more arXiv-issued DOI via DataCite
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