Abstract: This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions, and the network analysis based
on graph theory to investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
Abstract: Brain functional networks based on resting-state EEG
data were compared between patients with mild Alzheimer’s disease
(mAD) and matched patients with amnestic subtype of mild cognitive
impairment (aMCI). We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions and the network analysis based
on graph theory to further investigate the alterations of functional
networks in mAD compared with aMCI group. We aimed at
investigating the changes of network integrity, local clustering,
information processing efficiency, and fault tolerance in mAD brain
networks for different frequency bands based on several topological
properties, including degree, strength, clustering coefficient, shortest
path length, and efficiency. Results showed that the disruptions of
network integrity and reductions of network efficiency in mAD
characterized by lower degree, decreased clustering coefficient, higher
shortest path length, and reduced global and local efficiencies in the
delta, theta, beta2, and gamma bands were evident. The significant
changes in network organization can be used in assisting
discrimination of mAD from aMCI in clinical.
Abstract: In this paper two models using a functional network
were employed to solving classification problem. Functional networks
are generalized neural networks, which permit the specification of
their initial topology using knowledge about the problem at hand. In
this case, and after analyzing the available data and their relations, we
systematically discuss a numerical analysis method used for
functional network, and apply two functional network models to
solving XOR problem. The XOR problem that cannot be solved with
two-layered neural network can be solved by two-layered functional
network, which reveals a potent computational power of functional
networks, and the performance of the proposed model was validated
using classification problems.