Functional nuclear magnetic resonance time sequence matching method

Abstract

The invention discloses a functional nuclear magnetic resonance time sequence matching method. The method comprises the first step of calculating the initial value of a coefficient matrix Beta, the second step of calculating an F distance Fdist between an observed signal time sequence Y and a designed time sequence X, the third step of randomly acquiring a new recording spot, the fourth step of calculating a new F distance Fdist, and the fifth step of outputting Beta and completing matching of Beta of Y and Beta of X. According to the method, in the fMRI time sequence matching process, a time-domain signal is subjected to quick discrete Fourier mode transformation, and then a frequency-domain sequence is obtained, so that phase information is eliminated completely to achieve the purpose that the phase difference between fMRI time sequences to be matched is eliminated. Compared with a current phase correction method, the method is simpler, and the consumed calculated quantity is less. According to the method, the discrete Fourier mode transformation is subjected to weight constraint to reduce the influence of a high-frequency part, low-frequency effective signals are prioritized, the position of the most interesting frequency is determined more explicitly, and therefore the probability that a 'false positive' voxel appears after an inspection result is activated is lowered.

Description

technical field [0001] The invention relates to a data processing technology, in particular to a matching method of functional nuclear magnetic resonance time series with the same frequency and different phases. Background technique [0002] Functional magnetic resonance (fMRI) technology is an important imaging technology for nondestructively studying human brain behavior and has broad and important application prospects. The fMRI signal is an observational signal that is difficult to interpret intuitively, so a reasonable analysis of the fMRI signal is of great significance, which is not only a hot but also a difficult issue. [0003] The fMRI technology presents the activity of the brain through the blood oxygen level-dependent effect of the brain. Through this effect, the changes in cerebral blood flow of the brain activity can be detected in real time. fMRI data has the following characteristics: [0004] 1) Delay characteristics. For a brain function stimulation, th...

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Problems solved by technology

Therefore, algorithms that are sensitive to dimensionality often encounter difficulties when applied to fMRI data

[0007] 3) Low signal-to-noise ratio

[0010] Although SPM is widely used, some fMRI signals are not well converted into brain region activation information, because the generalized linear model (SPM-GLM) it relies on has strict requirements on the time series of stimulation, which greatly limits fMRI The flexibility of the experimental design, and the method regards the fMRI time series as a vector, and evaluates the norm distance between the fitting reference sequence and the time series signal to be measured as the objective function, that is, the matching of the fMRI signal to be measured and the model reference time series degree, to evaluate the purpose of optimization

However, as a time series, the connection between adjacent time points is very close, and fMRI signals as a time series, the individual differences in the delay of the signal triggered by the stimulus are relatively large, even if the same individual responds to different stimuli, there are differences in the delay, Therefore, when there is a large uncertainty difference between the time of brain neuron activity and the time of stimulus presentation, that is, when the time series of brain activity itself is difficult to describe accurately, the traditional SPM-GLM method is difficult to obtain ideal results.

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