Understanding the intricate functionality of the cerebral cortex requires integrating local circuit properties with global brain dynamics. Our study draws insights from biophysically detailed models of spiking neurons containing specific neural mechanisms, such as M-current and adaptation currents (Dalla Porta et al. Plos Comp. Bio., 2023). We incorporated these features in a mean-field model and, using human tractography data, we constructed a virtual whole-brain model consisting of 68 cortical nodes (Goldman et al. Front. Comput. Neurosci., 2023). To enhance realism, we introduced heterogeneity into each node, reflecting the varying distribution of M1 and M2 muscarinic receptors across cortical regions (Hawrylycz et al. Nature, 2012). This heterogeneity was integrated by adjusting local node functional properties, thereby creating a more detailed virtual brain landscape. Our exploration revealed that such heterogeneity profoundly impacts cortical properties, especially in the synchronous state. We observed a marked differentiation in properties like excitability and the degree of synchrony. In the asynchronous state, this diversity led to an increase in the differentiation of these cortical properties. Furthermore, we quantified global brain complexity using the perturbational complexity index (Gaglioti et al. Applied Sciences, 2024) to differentiate brain states and assess the impact of cholinergic heterogeneity. Our findings indicate a significant increase in complexity during the asynchronous state in the heterogeneous model, suggesting a closer approximation to real brain dynamics. This increased complexity reflects the spatiotemporal diversity of patterns and the intricate causal interactions across different cortical areas. Funded by CORTICOMOD PID2020-112947RB-I00 financed by MCIN/ AEI /10.13039/501100011033