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Abstract:
Mild cognitive impairment (MCI) is highly likely to convert to Alzheimer’s disease (AD). The main approach to identifying MCI is using a functional connection network (FCN). Traditional FCN is used to study the correlation between two brain regions, but it lacks deeper brain interaction information. Neuroscientists found the internal functional activity pattern in the human brain is characterized by sparse, modular, and overlapping structures, and the FCN is restricted by the brain structural connection network (SCN). They can improve the estimation accuracy of FCN. Therefore, this article first constructs low order FCN (LFCN) based on brain sparse, modular, and overlapping activity patterns. Then, new high-order FCN (HFCN) is proposed based on the restrictive relationship between SCN and FCN. To combine high robustness of LFCN with high sensitivity of HFCN, a new combination strategy of LFCN and HFCN is proposed. It integrates the idea of brain modular and overlapping with the restricted relationship between SCN and FCN. Finally, the experimental results show that in early MCI (EMCI) recognition the best classification performance is acquired with an accuracy of 91.42%, which is better than similar methods. This method will be instrumental in the early recognition of clinical MCI.
