The human brain is one of the most complex systems in nature, where tens of billions of neurons are interconnected by hundreds of trillions of synapses, forming an enormous network that is the structure basis for information processing and cognitive expressions of the human brain. With the depth of brain science, increasing evidence have demonstrated that the brain structural network and functional network is inseparable, with the functional network being based on the structured network. Functional brain network is the intuitive description of dynamic activities or mutual integration between different brain areas or neurons; it coordinates the different neurons or brain areas. Based on the indicator of blood-oxygen-level dependent, functional magnetic resonance imaging (fMRI) technology can be utilized to construct the functional brain network, whereby studies have found that the brain is a system intrinsically operating on its own, primarily driven by internal dynamics, with the external events modulating rather than determining the activity of the system. Therefore, by analyzing the spontaneous activity of the brain, more comprehensive information can be obtained regarding the brain information processing mechanisms, the brain basis of cognitive-behavioral, and neurological and psychiatric disorders in the brain dysfunction. With the clinical application in neurological and psychiatric disorders as the main goal and the combination of resting state fMRI technology, image processing technology, signal processing technology and machine learning methods as a breakthrough, we focused on pattern classification algorithm, function network-based brain disease abnormality identification and discriminant analysis, the development of language network. With resting-state fMRI technique, researchers have found spontaneous fluctuations in space consistency, i.e., the functional connectivity. The resting-state functional connectivity has been found to be related to pathological changes of some neuropsychiatric diseases. In recent years, machine learning has been a powerful data analysis tool analyzing functional magnetic resonance data. Different from traditional univariate approaches, machine learning involves multivariate pattern analysis that can fully exploit the multivariate nature of fMRI data for the early diagnosis of brain diseases. In this paper, we present an adaptive learning algorithm-based pattern classification algorithm. The main feature of this al...
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