[论文解读] A Dual-Head Transformer-State-Space Architecture for Neurocircuit Mechanism Decomposition from fMRI

Cole Korponay|arXiv (Cornell University)|Jan 20, 2026

Functional Brain Connectivity Studies被引用 0

一句话总结

简述：引入 Neurocircuit Mechanism Decomposition (NMD)，一种双头模型，将图约束延迟感知变换器与测量感知状态空间模型相结合，将 fMRI 功能连接分解为驱动、输入响应性和调节门控，以获得定向治疗洞见。

ABSTRACT

Precision psychiatry aspires to elucidate brain-based biomarkers of psychopathology to bolster disease risk assessment and treatment development. To this end, functional magnetic resonance imaging (fMRI) has helped triangulate brain circuits whose functional features are correlated with or even predictive of forms of psychopathology. Yet, fMRI biomarkers to date remain largely descriptive identifiers of where, rather than how, neurobiology is aberrant, limiting their utility for guiding treatment. We present a method for decomposing fMRI-based functional connectivity (FC) into constituent biomechanisms - output drive, input responsivity, modulator gating - with clearer alignment to differentiable therapeutic interventions. Neurocircuit mechanism decomposition (NMD) integrates (i) a graph-constrained, lag-aware transformer to estimate directed, pathway-specific routing distributions and drive signals, with (ii) a measurement-aware state-space model (SSM) that models hemodynamic convolution and recovers intrinsic latent dynamics. This dual-head architecture yields interpretable circuit parameters that may provide a more direct bridge from fMRI to treatment strategy selection. We instantiate the model in an anatomically and electrophysiologically well-defined circuit: the cortico-basal ganglia-thalamo-cortical loop.

研究动机与目标

• 通过将 fMRI 生物标志物与可操作的神经回路机制联系起来来推动精准精神医学。
• 将 fMRI 功能连接分解为输出驱动、输入响应性和调制门控。

提出的方法

• Develop a graph-constrained, lag-aware transformer to estimate directed, pathway-specific routing distributions and drive signals.
• Integrate with a measurement-aware state-space model to capture hemodynamic convolution and recover latent dynamics.
• Provide an interpretable set of circuit parameters bridging fMRI data and treatment strategy.

实验结果

研究问题

• RQ1 如何将基于 fMRI 的功能连接分解为可识别的神经回路机制？
• RQ2 双头架构能否产生与治疗干预对齐的可解释、机制层参数？
• RQ3 延迟感知变换器和测量感知 SSM 在从 fMRI 恢复内在神经动力学中的作用？
• RQ4 该方法如何应用于皮质-基底节-丘脑-皮质回路？

主要发现

• 提出一种双头架构，产生用于神经回路机制分解的可解释电路参数。
• 将图约束的延迟感知变换器与测量感知状态空间模型相结合，以建模血流动力学和潜在动力学。
• 在一个明确的皮质-基底节-丘脑-皮质回路中进行实例化演示。
• 提供一个框架，超越描述性生物标志物，指向机制驱动的治疗指引。