Brain function connectivity detection system and method based on self-adaptive priori information guidance

Abstract

The invention relates to a brain function connectivity detection system based on self-adaptive priori information guidance and a brain function connectivity detection method utilizing the system. The method comprises the steps that S1, blind source signal separation is performed separately on functional magnetic resonance data of all single-subjects in group-subjects collected by the same mask through an independent component analysis method, so independent functional components corresponding to all the single-subjects are obtained; S2, adaptive prior information used for guiding functional magnetic resonance data analysis on the group-subject and single-subject levels is extracted from the functional components corresponding to all the single-subjects; S3, by utilizing the adaptive prior information, based on a multi-objective optimization framework, in combination with a weight summing algorithm and a fast fixed-point algorithm, blind source signal separation is performed on the functional magnetic resonance data on the group-subject level, group functional components reflecting all subject commonalities in the group are obtained, so that brain function connectivity detection is completed. The brain function connectivity detection system can position a brain function connectivity area more accurately.

Description

technical field [0001] The invention relates to a detection system and method for brain functional connectivity, specifically a system and method for detecting brain functional connectivity based on adaptive prior information guidance, which belongs to the blind source signal separation technology based on functional magnetic resonance imaging technology . Background technique [0002] Functional magnetic resonance imaging technology is a new type of magnetic resonance imaging technology that began to emerge in the 1990s. This technology combines the information of function, anatomy and imaging, not only can display the location, size and range of brain function activation area, but also can directly display the exact anatomical location of the activation area, which is a breakthrough for traditional magnetic resonance technology from a single form. Strong technical support is provided from structural research to systematic research combining form and function. In addition...

AI Technical Summary

Problems solved by technology

Although there are currently many analysis methods for processing fMRI data, such as correlation methods, clustering methods, independent component analysis methods, and sparse methods, and they can all achieve the detection of brain functional connectivity to a certain extent, but in There are still shortcomings and defects in the application process, and the accuracy of brain function detection needs to be further improved

For example, the fuzzy clustering analysis method is limited by the iteration speed, fuzzy index, and the number of estimated functional areas; the independent component analysis method requires a strong assumption of mutual independence of functional source signals, which limits its detection ability in functionally connected areas

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