Chain-of-thought (CoT) reasoning is commonly improved through external mechanisms such as prompting, sampling, and decoding, while the internal units shaping reasoning trajectories remain less understood. We study CoT from the perspective of reasoning trajectory structure and find that incorrect trajectories are more redundant and repetitive, whereas correct ones are more lexically diverse and information-dense. Motivated by this observation, we propose Contrastive Neuron Regulation (CNR), a sparse, backbone-frozen framework for neuron-level regulation of CoT reasoning. CNR constructs correctness-controlled expanded–concise pairs of correct trajectories, identifies MLP neurons associated with the expansion contrast, and sparsely suppresses them during generation. We consider both a fixed-coefficient inference-time intervention and an adaptive variant that learns neuron-specific suppression coefficients on a subject-specific development split while keeping the backbone frozen. Across two reasoning models and seven MATH subjects, CNR improves macro-average accuracy over the no-intervention baseline by 2.6 and 3.2 percentage points, respectively, while intervening on less than 1% of MLP neurons. The code is available at https://github.com/RUCAIBox/Contrastive-Neuron-Regulation.