Abstract
The aim of this study was to analyze the application value of functional magnetic resonance imaging (FMRI) optimized by the fast independent component correlation algorithm (ICA algorithm) in the diagnosis of brain functional areas in patients with lumbar disc herniation (LDH). An optimized fast ICA algorithm was established based on the ICA algorithm. 50 patients with cerebral infarction were selected as the research objects, and 30 healthy people were selected as the control group. The 50 patients from the observation group were examined by fMRI based on Fast ICA algorithm, while the control group was tested by fMRI based on the routine ICA algorithm. The performances of the two algorithms, the analysis results of the two groups of brain functional areas, cerebral blood flow (CBF), resting state functional connectivity (rsFC), behavioral data, and image data correlation of patients were compared. The results showed that the sensitivity, specificity, and accuracy of Fast ICA algorithm were 97.83%, 89.52%, and 96.27%, respectively, which in the experimental group were greatly better than the control group (88.73%, 72.19%, and 89.72%), showing statistically significant differences (P < 0.05). The maximum Dice coefficient of FAST ICA algorithm was 0.967, and FAST ICA algorithm was better obviously than the traditional ICA algorithm (P < 0.05). The cerebral blood flow of the healthy superior frontal gyrus (SFG) and healthy superior marginal gyrus (SMG) of the observation group with good motor function recovery were 1.02 ± 0.22 and 1.53 ± 0.61, respectively; both indicators showed an increasing trend, and those in the experimental group were much higher in contrast to the control group, showing statistically obvious differences (P < 0.05). Besides, the detection results of cerebral blood flow (CBF) in the healthy SFG and healthy SMG were negatively correlated with the results of connection test B. In summary, the fMRI based on the Fast ICA algorithm showed a good diagnostic effect in the changes of brain functional areas in patients with cerebral infarction. The experimental results showed that the cerebral blood flow in the brain area was related to motor or cognitive function. The results of this study provided a reliable reference for the examination and diagnosis of brain functional areas in patients with cerebral infarction.
Highlights
In recent years, with the continuous improvement of people’s living standards, the incidence of heart and brain diseases has increased year by year, especially the number of patients with cerebral infarction
* Observation group Control group (b) and accuracy of the Fast independent component analysis (ICA) algorithm in the experimental group were 97.83%, 89.52%, and 96.27%, respectively, which were greatly better than those of the control group, showing statistically significant differences (P < 0.05). e maximum Dice coefficient of Fast ICA algorithm was 0.967, while the maximum Dice coefficient of traditional ICA algorithm was 0.738. e area under the curve (AUC) values of FAST ICA algorithm and traditional ICA algorithm were 0.978 and 0.773 in turn, so the FAST ICA algorithm was obviously better than traditional ICA algorithm (P < 0.05). e research results were similar to the research findings of Zan et al [22], and both showed that the diagnostic accuracy and other performance of the optimized Fast ICA algorithm were significantly improved
Patients with poor motor function recovery would have continuous low perfusion state in the motor sensory cortex. It meant that the imbalance of blood perfusion ratio in the sensorimotor cortex was likely to cause poor motor function recovery after cerebral infarction. e correlation analysis of this study found that there was a negative correlation between cerebral blood flow (CBF) values of healthy superior marginal gyrus (SMG) and healthy superior frontal gyrus (SFG) and the connection TMT-B
Summary
With the continuous improvement of people’s living standards, the incidence of heart and brain diseases has increased year by year, especially the number of patients with cerebral infarction. E plasticity and reorganization ability of the brain after cerebral infarction is a necessary condition for the recovery of patients’ neurological function, which is currently a hotspot of neuroscience research. It has important research significance and clinical significance and helps to promote the prognosis and rehabilitation of patients [5, 6]. E research on changes and reorganization of brain function after cerebral infarction is of practical value in evaluating the prognosis and functional recovery of patients [7]. Restingstate functional connectivity (rsFC) analysis method is often applied in the overall study of brain networks based on resting-state fMRI [9, 10]
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