Modelling brain signals play a crucial role in analysing the brain's architecture, functions and associated disorders. This paper aims to model the brain topology by exploring the relationship between complex neural correlates and functional connectivity-based distances. A computational model inspired by multivariate visibility graphs (VG) algorithm and Euclidean distance is proposed to analyse quantitatively the brain network data. When applied to resting-state EEG signals from three groups (typically developing (TD), autism spectrum disorder (ASD), and epilepsy (E)), the network topological properties (e.g., global efficiency, modularity, small-worldness, and betweenness centrality) demonstrate variations in connectivity distance probabilities among brain regions (e.g., frontal, temporal, parietal, and occipital) via the model's delay and connection distance parameters. The results showed a higher delay and skewed distribution towards short functional connections in ASD than in TD, while a lower delay in E than in ASD and TD. Additionally, ASD had more short-distance connections, while E had more long-distance connections compared to TD. ASD and E significantly overlapped over short-distance connections within the temporal lobe. In summary, the proposed model illustrates that delay parameter and connection distance obtained from brain network data have the potential to objectively identify and associate co-occurring neurological conditions (e.g., ASD and E).