Evaluation on large-scale motor and cognitive network degeneration in patients with amyotrophic lateral sclerosis by independent component analysis and dual regression based on MRI / 中华放射学杂志

ABSTRACT

Objective:To evaluate changes of large-scale motor and cognition related networks′ function in patients with amyotrophic lateral sclerosis (ALS) and their relationship with corresponding clinical symptoms using independent component analysis combined with dual regression.Methods:Forty-six ALS patients (ALS group) who were treated in the First Affiliated Hospital of Xi′an Jiaotong University from January 2014 to June 2016 were prospectively collected, and 40 gender- and age-matched normal controls (control group) were recruited. All the participants completed the motor and multi-dimensional cognitive function evaluation[including Mini-mental State Examination (MMSE), Montreal Cognitive Assessment (MoCa), Semantic Fluency (SVF), Phonological Fluency (PVF), Digital Span Forward (DS_F), Digital Span backward (DS_B), frontal assessment battery (FAB), Wisconsin Card Sorting Test (WCST) for classification accuracy, classification error, persistent response classification, persistent error response classification, non-persistent error classification and Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA)]. The resting-state MRI data of all subjects were collected, and independent component analysis was carried out with multivariate interpretation linear optimization independent component decomposition. Dual regression analysis was performed to compare network differences between groups based on voxel level in sensorimotor network (SMN), default mode network (DMN) and frontal-parietal control network (FPCN). Multivariate covariance analysis was used to evaluate the differences of different cognitive function indexes between ALS group and normal control group, the comparison of brain network differences between the two groups was performed by nonparametric permutation test, corrected by family-wise error (FWE), P<0.008 as the statistical threshold; partial correlation and multiple linear regression were used to evaluate the relationship between changes in functional connectivity of different brain regions and cognitive functions. Results:The scores of MMSE, MoCa, SVF, PVF, DS_B, and classification accuracy were lower, while the number of error classifications, the non-persistent error classifications, HAMD and HAMA scores were higher in patients with ALS group than those in control group ( P<0.05). After adjusting for gender and age, there was no significant difference in the SMN between ALS group and control group (FWE correction, P>0.008). Compared with control group, patients with ALS showed increased functional connectivity in the left ventromedial prefrontal cortex (vmPFC) of the DMN, and decreased functional connectivity in the right anterior cingulate gyrus (ACC), the right posterior cingulate gyrus, the left inferior parietal lobule and the left inferior temporal gyrus of the FPCN (FWE correction, P<0.008). Increased functional connectivity of the vmPFC in ALS patients was negatively correlated with MoCa score ( r=-0.565, P<0.001), FAB score ( r=-0.373, P=0.015) and the classification accuracy of WCST ( r=-0.478, P=0.002), SVF ( r=-0.458, P=0.002) scores, and was positively correlated with the number of error classifications and HAMA scores ( r=0.416, P=0.007; r=0.388, P=0.011). Decreased functional connectivity were detected in multiple brain regions of FPCN, and the functional connectivity of the ACC was positively correlated with the DS_F ( r=0.341, P=0.027) and MMSE ( r=0.351, P=0.023). The effect of increased vmPFC functional connectivity accounted for 49.6% changes on MoCa score; 35.2% and 34.2% for FAB and HAMA respectively. While the impact of increased functional connectivity in the vmPFC was less than 30% on classification accuracy, classification error of WCST and SVF. The reduced functional connectivity in the ACC accounted for 37.7% impact on the DS_F score. Conclusions:Large-scale brain network changes are dominated by the frontotemporal core brain regions in ALS patients. DMN and FPCN network changes are closely related to the clinical cognitive performance of ALS patients.