A discrete-continuous-time stochastic framework for attention-deficit/hyperactivity disorder-related symptom modeling and pharmacological intervention via methylphenidate

Oana-Maria-Catalina Crisan

cs.LGOct 18, 2026

A discrete-continuous-time stochastic framework for attention-deficit/hyperactivity disorder-related symptom modeling and pharmacological intervention via methylphenidate

Authors: Oana-Maria-Catalina Crisan

Organizations: Faculty of Mathematics and Computer Science, Babes,-Bolyai University, Str. Mihail Kog˘alniceanu, nr. 1, Cluj-Napoca, 400084, Romania.

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.

Figures & tables

Panel of Figure 1 — Figure 1: Baseline saliency (left) and global network activations (right) for patient 2026113.

Panel of Figure 2 — Figure 2: Pre and post-intervention spatial activations for patient 2026113, displaying structural normalization under optimal MPH dosage. Rows from top to bottom: attention decay, motivation loss, hyperactivity and impulsivity: left column - baseline state, right column - medicated state.

Panel of Figure 3 — Figure 3: Baseline saliency (left) and global network activations (right) for patient 1992284.

Panel of Figure 4 — Figure 4: Pre- and post-intervention spatial activations for patient 1992284, displaying structural normalization under optimal MPH dosage. Rows from top to bottom: attention decay, motivation loss, hyperactivity and impulsivity: left column - baseline state, right column - medicated state.

Abbreviation	Definition
1D-CNN	One-dimensional convolutional neural network
ACC	Anterior cingulate cortex
ADHD	Attention-deficit/hyperactivity disorder
ADHD-C	Combined presentation of ADHD
ADHD-HI	Predominantly hyperactive/impulsive presentation of ADHD
ADHD-I	Predominantly inattentive presentation of ADHD

Table 3: List of abbreviations

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Mental health research studies psychological well-being, distress, disorders, their causes, assessment, prevention, and treatment.

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DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI

Qurat Ul Ain, Alptekin Temizel, Soyiba Jawed

Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey

Brain Disorder Classification Classification Explainable Medical Image Analysis Brain MRI

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Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of reliable imaging-based biomarkers, particularly anatomical markers. Structural MRI (sMRI) provides a non-invasive modality for investigating brain alterations associated with ADHD; nevertheless, most deep learning approaches function as black-box systems, limiting clinical trust and interpretability. In this work, we propose DuSCN-FusionNet, an interpretable sMRI-based framework for ADHD classification that leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships. ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs, which are processed through an SCN-CNN encoder. In parallel, auxiliary ROI-wise variability features and global statistical descriptors are integrated via late-stage fusion to enhance performance. The model is evaluated using stratified 10-fold cross-validation with a 5-seed ensemble strategy, achieving a mean balanced accuracy of 80.59% and an AUC of 0.778 on the Peking University site of the ADHD-200 dataset. DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively. Moreover, Grad-CAM is adapted to the SCN domain to derive ROI-level importance scores, enabling the identification of structurally relevant brain regions as potential biomarkers.

Abstract

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