Check if any spelling errors and descriptions that can be improved in the following paragraphs from the perspective of academic papers, and rewrite them in a more professional, refined and academic way: 
Recent investigations from the graph learning communities have shown that, learning features in the spectral domain might not exploit the graph structure well and less adaptively to graph properties for discriminative representation learning [21, 22, 23]. Different from spectral GNNs,  spatial-based convolutional GNNs (i.e., spatial GNNs) implement the neighborhood aggregation based on the spatial relations of nodes with explicit consideration of graph structures. On the other hand, the indistinct early symptoms and heterogeneous properties of MCI [24, 25] makes it a potential way to use spatial GNNs to obtain more discriminative representations from the raw BFC graphs. Specifically, recent investigations have revealed that the small inter-group variation problem and the large intra-group discrepancy problem are widely spread in BFC graphs generating from either scientific or clinical fMRI data [25, 26]. In other words, the differences between BFC graphs of early MCI group and those of normal aging group can be very subtle, yet between individual subjects in the same group can be sizeable. In this case, the critical discrepancy between BFC graphs in different groups may lie in some local sub-structures, while in the same group may lie in the global structural properties. Therefore, it is worthwhile exploring gr