Background: Leukoaraiosis (LA) patients may suffer from sensorimotor dysfunctions. The relationship between behavioral disturbances and changes in the sensorimotor network (SMN) has not been thoroughly elucidated. Objective: This study investigated the hypothesized breakdown of communication of SMN and its behavioral consequences in LA. Methods: Fluid-attenuated inversion recovery (FLAIR) images, resting-state functional magnetic resonance images (fMRI) and behavioral data were collected from 30 LA patients and 26 healthy individuals (normal controls, NC). The subjects were grouped according to LA severity, as indicated by their FLAIR images. Group independent component analysis was applied to the fMRI data to map the functional connectivity of SMN for NC and LA patients. A whole-brain, voxel-wise analysis was employed to investigate the functional connectivity alteration of SMN in LA. The relationships between LA severity, functional connectivity alteration of the SMN and behavioral clinical symptoms were examined by correlation analysis. Results: The right cingulate motor area (rCMA), left posterior insula and left ventral premotor area showed attenuated functional connectivity in the LA patients. The extent of the attenuation was related to the severity of the disease. Furthermore, the attenuation in the rCMA was associated with worse sensorimotor integration performance. Conclusions: These results suggest that LA impairs sensorimotor integration by interfering with the communication or coordination of these aforementioned regions related to the SMN.

Leukoaraiosis (LA) describes diffuse white matter abnormalities apparent in computed tomography (CT) or magnetic resonance (MR) brain scans. Patients with LA generally show varying degrees of cognitive impairment, which can be classified as cognitively normal (CN), mild cognitive impairment (MCI), and dementia. However, a consistent relationship between the degree of LA and the level of cognitive impairment has not yet been established. We used functional magnetic resonance imaging (fMRI) to explore possible neuroimaging biomarkers for classification of cognitive level in LA. Functional connectivity (FC) between brain regions was calculated using Pearson’s correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC). Next, FCs with high discriminative power for different cognitive levels in LA were used as features for classification based on support vector machine. CN and MCI were classified with accuracies of 75.0, 61.9, and 91.1% based on features from PCC, MIC, and eMIC, respectively. MCI and dementia were classified with accuracies of 80.1, 86.2, and 87.4% based on features from PCC, MIC, and eMIC, respectively. CN and dementia were classified with accuracies of 80.1, 89.9, and 94.4% based on features from PCC, MIC, and eMIC, respectively. Our results suggest that features extracted from fMRI were efficient for classification of cognitive impairment level in LA, especially, when features were based on a non-linear method (eMIC).

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