The effect of brain microbleeds on the structural brain network after stroke
Xiaopei Xu1, Henry KF Mak1, Raja Rizal Azman2, Kui-Kai Lau3, and Edward S Hui1

1Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong, 2Department of Bio-Medical Imaging, University of Malaya, Kuala, Malaysia, 3Department of Medicine, The University of Hong Kong, Hong Kong, Hong Kong


To explore the influences of brain microbleeds (BMBs) on structural brain network in stroke patients, we used DTI-based tractography and brain network analysis to investigate the brain structural network configuration in stroke patients with and without BMBs. Our results demonstrated disrupted balance between integrated and segregated process in global network, and enhanced specialized functions in stroke patients with BMBs. These findings suggested that the presence of BMBs is related to a disturbed brain network organization with imbalanced integrated and segregated information process ability, and brain network analysis is a sensitive tool to assess the impaired cognition caused by cerebral small vessel disease.


Brain microbleeds (BMBs), often found in stroke patients, are widely accepted as a manifestation of cerebral small vessel disease1. BMBs are related to white matter changes and reflect diffuse pathology2. They are also a major risk factor for vascular dementia3. A previous stroke study4 indicated that BMBs might play an important role in post-stroke cognitive impairment5. Because the mechanism by which BMBs affects cognition remains elusive, it is imperative that we understand their effect on the structural brain network of stroke patients at a global level.

Materials and Methods

Participants 48 stroke patients (63 ± 14 years old) were recruited.

Image acquisition DWIs were acquired using single-shot EPI with b values of 1000 and 2000 s/mm2 along 32 gradient directions using a 3T scanner (Achieva TX scanner, Philips Healthcare).

Post-processing 3D-MPRAGE images were segmented into 90 brain regions according to AAL atlas. Whole-brain tractography was obtained using Diffusion Toolkit ( Fiber tracts traversing two regions were counted, resulting in a connectivity matrix. Network metrics, such as clustering coefficient, characteristic path length, normalized clustering coefficient (γ), normalized characteristic path length (λ), global and local efficiency were obtained using the Brain Connectivity Toolbox6.

BMBs scoring SWI images were examined by a radiologist to score BMBs using the Microbleed Anatomical Rating Scale7 (MARS). 12 patients had MARS = 1, and 3 had MARS = 2.

Statistical Analysis Repeated measures ANOVA (age and gender as covariates) followed by post-hoc independent samples t-tests were performed. Pearson correlations were performed to determine the association between network metrics and MARS.


Global network Larger clustering coefficient and local efficiency were observed for patients with MARS = 1 (both p < 0.001) and MARS = 2 (p = 0.007 and 0.017, respectively) as compared to those without BMBs. Clustering coefficient (r = 0.69, p < 0.001) and nodal efficiency (r = 0.73, p < 0.001) were correlated with MARS.

Nodal characteristics The connections amongst the regions identified as hubs in normal subjects (data from another study) and stroke patients without BMB (red spheres in the top row of Figure 1) are more clustered in patients with BMBs. Larger local clustering coefficient were found in the superior frontal cortex (MARS = 1: p = 0.006; MARS = 2: p = 0.016), hippocampus (MARS = 1: p = 0.009; MARS = 2: p = 0.036), precuneus (MARS = 1: p = 0.039; MARS = 2: p = 0.025), putamen (MARS = 1: p < 0.001; MARS = 2: p < 0.001) and globus pallidus (MARS = 1: p = 0.001; MARS = 2: p = 0.001). Higher nodal efficiency was observed in insula (MARS = 2: p = 0.034), hippocampus (MARS = 2: p = 0.019), and precuneus (MARS = 1: p = 0.003; MARS = 2: p = 0.004).


The balance between segregation and integration, as achieved by the coordination of activities amongst various neural components, is fundamental in maintaining normal brain functioning8, and the disruption thereof could lead to dysfunction of cognition and behavior9. The fact that we observed significant correlation between segregation measurements and the MARS of stroke patients suggests that the presence of BMBs may cause structural network to be more functionally segregated and have stronger local connectivity (in other words, loss of long-range connections). We also observed increased local clustering coefficient and nodal efficiency of some brain hubs in patients with BMBs, further supporting the notion that the connections amongst the hubs that share similar function are strengthened. Similar alteration in network organization was also observed in various neurological and psychiatric disorders10-12.

Another noteworthy finding is that some of the brain hubs of patients without BMBs were replaced by regions such as the calcarine sulcus and lingual gyrus for patients with BMBs, suggesting an enhancement in specialized functions such as visual and language function due to the presence of BMBs. Similar enhancement in specialized functions is also found in patients with autism spectrum disorders13 whom had relatively good performance of language function. In other words, the balance between segregation and integration for stroke patients with BMBs is clearly disrupted, thus causing cognitive impairment (although we don’t have any cognitive measures to corroborate our findings). Indeed, when brain could not fulfill cognitive demand, it would develop into a more clustered and less costly configuration14.


Our findings suggested that the presence of BMBs disrupts the balance between the ability to process integrated and segregated information for stroke patients, and that brain network analysis is a sensitive method to assess cognitive impairment caused by cerebral small vessel disease.


No acknowledgement found.


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Structural brain network of patient without and with brain microbleeds (BMBs). Red and green spheres are network hubs and non-hub brain regions, respectively. Sphere size represents nodal efficiency, and width of blue lines represents the number of connections between regions. Nodal efficiency and local connections of patients with BMBs were increased in the middle occipital gyrus (MOG), calcarine sulcus (CAL) and lingual gyrus (LING) compared to those without BMBs. MTG: middle temporal cortex.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)