Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have been widely employed to model complex brain networks. However, leveraging the complementary temporal, spatial, and spectral features for diagnosing neurocognitive disorders remains challenging. To address this issue, we propose a Bi-path Multi-scale Graph Neural Network (Bi-MCGNN), which integrates two paths : one focusing on time and spatial relationships, and the other on spatial and frequency patterns. By unifying these pathways, Bi-MCGNN integrates diverse brain features into a single framework. In order to more effectively represent brain networks, we designed specialized correlation matrixs to reinforce the constructed graph. We then performed multi-scale graph convolution to analyze brain connectivity at varying resolutions-from fine-grained to more extensive patterns, and ultimately employed an attention mechanism to enhance features across different domains. Extensive experiments on two real-world datasets demonstrate that our model outperforms state-of-the-art baselines.