Aberrant white matter networks mediate cognitive impairment in patients with silent lacunar infarcts in basal ganglia territory

Disrupted white matter structural networks in patients with acute ischemic stroke in the right basal ganglia

Widespread structural changes have been observed in patients with stroke in previous diffusion tensor imaging studies. However, the topological organization of white matter structural networks after acute ischemic stroke (AIS) in the right basal ganglia (BG) remains unknown. The aim of our study is to investigate whether the topological structure of the white matter structural network is altered in patients with AIS in the right BG, and its relationship with cognition. Graph theoretical analysis was employed to investigate the topological architecture of whole-brain white matter structural networks in 40 AIS patients in the right BG and 40 healthy controls (HC), and network-based statistics (NBS) were applied to examine structural connectivity alterations. Compared to HC, AIS patients exhibited altered global network properties characterized by increased small-worldness, normalized clustering coefficient, and shortest path length, as well as decreased clustering coefficient, local efficiency, and global efficiency. The nodes with significantly decreased nodal properties in AIS patients were primarily located in the default mode network, limbic system, sensorimotor system, salience network, and central executive network. Reduced structural connectivity detected by NBS in AIS patients were primarily located in the lesional hemisphere. Furthermore, altered nodal properties were correlated with cognitive scores. Documenting the alterations in the topological patterns of white matter structural networks will help to promote the understanding of the neural mechanisms of cognitive impairment after AIS in the right BG.

Brain Structure-Function Fusing Representation Learning Using Adversarial Decomposed-VAE for Analyzing MCI

Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse structural and functional features in exploring the complex brain network. In this paper, a novel brain structure-function fusing-representation learning (BSFL) model is proposed to effectively learn fused representation from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) for mild cognitive impairment (MCI) analysis. Specifically, the decomposition-fusion framework is developed to first decompose the feature space into the union of the uniform and unique spaces for each modality, and then adaptively fuse the decomposed features to learn MCI-related representation. Moreover, a knowledge-aware transformer module is designed to automatically capture local and global connectivity features throughout the brain. Also, a uniform-unique contrastive loss is further devised to make the decomposition more effective and enhance the complementarity of structural and functional features. The extensive experiments demonstrate that the proposed model achieves better performance than other competitive methods in predicting and analyzing MCI. More importantly, the proposed model could be a potential tool for reconstructing unified brain networks and predicting abnormal connections during the degenerative processes in MCI.

Clinical Features, Brain-Structure Changes, and Cognitive Impairment in Basal Ganglia Infarcts: A Pilot Study

Introduction

Stroke has been considered to raise the risk of dementia in several studies, but the relationship between brain structural changes and poststroke cognitive impairment (PSCI) is unclear.

Methods

In this study, 23 PSCI patients with basal ganglia infarcts after 2 weeks and 29 age-matched controls underwent magnetic resonance imaging measuring cortical thickness and volume changes, as well as neuropsychological tests. CI was derived from a performance score <1.5 standard deviations for normally distributed scores. We compared Z scores in different cognitive domains and cortical thickness and volumes in two groups. Multiple linear regressions were used to investigate the relationship between cortical thickness and volumes and neuropsychological tests.

Results

A majority of PSCI patients were in their 50s (55.19±8.52 years). PSCI patients exhibited significantly decreased Z scores in multiple domains, such as memory, language, visuomotor speed, and attention/executive function. The volumes of the middle posterior corpus callosum, middle anterior corpus callosum, and hippocampus in PSCI patients were markedly lower than controls. The thickness of the right inferior temporal cortex and insula were significantly smaller than controls. It found that the reduced right hippocampus was related to executive dysfunction. Hippocampus dysfunction may be involved in language impairment (p<0.05) in PSCI patients with basal ganglia infarcts.

Conclusion

These findings demonstrated that brain structure changed after ischemic stroke, and different gray-matter structural changes could lead to specific cognitive decline in PSCI patients with basal ganglia infarcts. Atrophy of the right hippocampus potentially serves as an imaging marker of early executive function of PSCI.

Rich-Club Analysis of the Structural Brain Network in Cases with Cerebral Small Vessel Disease and Depression Symptoms

BACKGROUND

Altered white matter brain networks have been extensively studied in cerebral small vessel disease (SVD). However, there exists currently a deficiency of comprehending the performance of changes within the structural networks of the brain in cases with cerebral SVD and depression symptoms. The main aim of the present research is to study the network topology behaviors and features of rich-club organization in SVD patients using graph theory and diffusion tensor imaging (DTI) to characterize changes in the microstructure of the brain.

METHODS

DTI datasets were acquired from 26 SVD patients with symptoms of depression (SVD + D) and 26 SVD patients without symptoms of depression (SVD - D), and a series of neuropsychological assessments were completed. A structural network was created using a deterministic fiber tracking method. The analysis of rich-club was performed in company with analysis of the global network features of the network to characterize the topological properties of all subjects.

RESULTS

DTI data were obtained from SVD patients who manifested symptoms of depression (SVD + D) and from control SVD patients (SVD - D). In comparison with SVD - D patients, SVD + D cases demonstrated a diminished coefficient of clustering along with lower global efficiencies and longer path length characteristics. Rich-club analysis showed SVD + D patients had decreased feeder connectivity and local connectivity strengths compared to SVD - D patients. Our data also showed that the feeder connections in the brain correlated significantly with the severity of depression in SVD + D patients.

CONCLUSIONS

Our study revealed that SVD patients with depressive symptoms have disrupted white matter networks that characteristically have reduced network efficiency compared to the networks in other SVD patients. Disrupted information interactions among the regions of nonrich-club and rich-club in SVD cases are related to the severity of depression. Our data suggest that DTI may be utilized as an appropriate biomarker for the diagnosis of depression in comorbid SVD patients.

Predicted disconnectome associated with progressive periventricular white matter ischemia

We used a virtual lesion DTI fiber tracking approach with healthy subject DTI data and simulated periventricular white matter (PVWM) lesion masks to predict the sequence of connectivity changes associated with progressive PVWM ischemia. We found that the optic radiations, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, corpus callosum, temporopontine tract and fornix were affected in early simulated ischemic injury, and that the connectivity of subcortical, cerebellar, and visual regions were significantly disrupted with increasing simulated lesion severity. The results of this study provide insights into the spatial-temporal changes of the brain structural connectome under progressive PVWM ischemia. The virtual lesion approach provides a meaningful proxy to the spatial-temporal changes of the brain's structural connectome and can be used to further characterize the cognitive sequelae of progressive PVWM ischemia in both normal aging and dementia.

Network reorganization during verbal fluency task in fronto-temporal epilepsy: A functional near-infrared spectroscopy study

This is the first study to use functional near-infrared spectroscopy (fNIRS) to investigate how the lateralization of the epileptogenic zone affects the reconfiguration of task-related network patterns. Eleven left fronto-temporal epilepsy (L-FTE) and 11 right fronto-temporal epilepsy (R-FTE), as well as 22 age- and gender-matched controls, were enrolled. Signals from 52-channel fNIRS were recorded while the subject was undertaking verbal fluency tasks (VFTs), which included categorical (CFT) and letter (LFT) fluency tasks. Three analytic methods were used to study the network topology: network-based analysis, hub identification, and proportional threshold to select the top 20% strongest connections for both graph theory parameters and clinical correlation. Performance of CFT is accomplished primarily using the ventral pathway, and bilateral ventral pathways are augmented in fronto-temporal epilepsy patients by strengthening the inter-hemispheric connections, especially for R-FTE. LFT mainly employed the dorsal pathway, and further prioritized the left dorsal pathway in strengthening intra-hemispheric connections in fronto-temporal epilepsy, especially L-FTE. The top 20% of the strongest connections only present differences in CFT network compared with the controls. R-FTE increased inter-hemispheric network density, while L-FTE decreased inter-hemispheric average characteristic path length. Accumulative seizure burden only affects L-FTE network. Better LFT performance and longer educational years seem to promote left fronto-temporal networks, and decreased the demand from RR intra-hemispheric connectivity in L-FTE. LFT scores in R-FTE are maintained by preserved RR intra-hemispheric networks. However, CFT scores and educational years seem to have no effect on the CFT network topology in both FTE.

Relation between left atrial structure and lacunar infarction in patients with hypertension

A lacunar infarction (LACI) can cause damage to the surrounding brain tissue and place an individual at greater risk for future major stroke. LACI is associated with hypertension and hypertension is associated with left atrial enlargement. It is important to identify a high-risk patient who is more vulnerable to suffering a LACI in hypertensive group. So, we studied whether left atrium size is an independent risk predictor for LACI in hypertensive patients. We performed cross-sectional analysis of 365 patients with hypertension at Shanghai Ninth People's Hospital from January 2016 to January 2017. The results showed that left atrial diameter(LAD), left atrial volume (LAV) and the ratio of left atrial diameter to left ventricular diameter (LAD/LVD) were significantly associated with LACI in hypertensive patients. Based on the ROC curve analysis, the area under the ROC curve (AUC) of LAV used to predict LACI was 0.737 (95% CI: 0.686 - 0.788), and the AUC of LAD/LVD was 0.784 (95% CI: 0.737 - 0.830). The optimal cut-off value for LAV was 30.14, and the sensitivity and specificity were 72% and 63%, respectively. The optimal cut-off value for LAD/LVD was 0.757, and the sensitivity and specificity were 77% and 70%, respectively. LAV or LAD/LVD played an important role in LACI with hypertension and could be an independent risk factor in hypertensive patients.

Cognitive declines after perioperative covert stroke: Recent advances and perspectives

PURPOSE OF REVIEW

With the aging of the population, there are increasing number of aged patients who require surgical interventions. Perioperative covert stroke is emerging as an important health threat and social burden that could affect patients' long-term neurological outcomes.

RECENT FINDINGS

Recent findings of the association between perioperative covert stroke with long-term cognitive declines of surgical patients highlighted the significance of the silent cognitive function killer-perioperative covert stroke. Considering the devastating long-term consequence of the asymptomatic covert stroke, early diagnosis and prevention are turning out as crucial problems to tackle. The evolving brain imaging techniques, such as multimodel MRI sequences are not only able to detect early, small and subtle injuries of the acute ischemic lesions, but also quite advantageous in capturing the preexisting brain vascular diseases that are considered as important risk factors of covert stroke. However, effective predictive markers are still lacking to identify high risk patients for perioperative covert stroke, rendering an unmet need of investigations in this regard.

SUMMARY

The present review will summarize recent findings in perioperative covert stroke and highlight future perspectives of its early diagnosis and the impact of postoperative cognitive impairments.

Cerebral small vessel disease: from a focal to a global perspective

Cerebral small vessel disease (SVD) is commonly observed on neuroimaging among elderly individuals and is recognized as a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. However, clinical symptoms are often highly inconsistent in nature and severity among patients with similar degrees of SVD on brain imaging. Here, we provide a new framework based on new advances in structural and functional neuroimaging that aims to explain the remarkable clinical variation in SVD. First, we discuss the heterogeneous pathology present in SVD lesions despite an identical appearance on imaging and the perilesional and remote effects of these lesions. We review effects of SVD on structural and functional connectivity in the brain, and we discuss how network disruption by SVD can lead to clinical deficits. We address reserve and compensatory mechanisms in SVD and discuss the part played by other age-related pathologies. Finally, we conclude that SVD should be considered a global rather than a focal disease, as the classically recognized focal lesions affect remote brain structures and structural and functional network connections. The large variability in clinical symptoms among patients with SVD can probably be understood by taking into account the heterogeneity of SVD lesions, the effects of SVD beyond the focal lesions, the contribution of neurodegenerative pathologies other than SVD, and the interaction with reserve mechanisms and compensatory mechanisms.

Basal Ganglia-Cortical Circuit Disruption in Subcortical Silent Lacunar Infarcts

To investigate the alterations of basal ganglia (BG)-cortical structural and functional connectivity induced by subcortical silent lacunar infarct (SLI), and their associations with cognitive impairment in SLI subjects. All participants were recruited from communities, including 30 subcortical SLIs and 30 age-, gender-, and education-matched healthy controls. The structural and functional connectivity of BG-cortical circuits using diffusion and resting-state functional magnetic resonance imaging data were obtained. Diffusion abnormalities of the white matter tracts connecting the BG and cortical areas were observed in SLI subjects, including the BG-lateral frontal, BG-orbital frontal, and BG-insula tracts. Multiple regions showed a reduced BG-cortical functional connectivity in SLI patients, including direct connectivities with the BG, such as the BG-limbic, BG-insula, and BG-frontal connectivities, and others that showed no direct causation with the BG, such as the insula-limbic, insula-parietal, and frontal-parietal connectivities. Coupling of structural and functional BG-cortical connectivity was observed in healthy controls but not in SLI patients. Significant correlations between structural and functional BG-cortical connectivity and cognitive performance were demonstrated in SLI patients, indicating the potential use of BG-cortical connectivities as MRI biomarkers to assess cognitive impairment. These findings suggest that subcortical SLIs can impair BG-cortical circuits, and these changes may be the pathological basis of cognitive impairment in SLI patients.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

White matter network topology relates to cognitive flexibility and cumulative neurological risk in adult survivors of pediatric brain tumors

Adult survivors of pediatric brain tumors exhibit deficits in executive functioning. Given that brain tumors and medical treatments for brain tumors result in disruptions to white matter, a network analysis was used to explore the topological properties of white matter networks. This study used diffusion tensor imaging and deterministic tractography in 38 adult survivors of pediatric brain tumors (mean age in years = 23.11 (SD = 4.96), 54% female, mean years post diagnosis = 14.09 (SD = 6.19)) and 38 healthy peers matched by age, gender, handedness, and socioeconomic status. Nodes were defined using the Automated Anatomical Labeling (AAL) parcellation scheme, and edges were defined as the mean fractional anisotropy of streamlines that connected each node pair. Global efficiency and average clustering coefficient were reduced in survivors compared to healthy peers with preferential impact to hub regions. Global efficiency mediated differences in cognitive flexibility between survivors and healthy peers, as well as the relationship between cumulative neurological risk and cognitive flexibility. These results suggest that adult survivors of pediatric brain tumors, on average one and a half decades post brain tumor diagnosis and treatment, exhibit altered white matter topology in the form of suboptimal integration and segregation of large scale networks, and that disrupted topology may underlie executive functioning impairments. Network based studies provided important topographic insights on network organization in long-term survivors of pediatric brain tumor.

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Long term brain tumor survivorship is associated with altered white matter networks.

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Hub regions were preferentially impacted in survivors.

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Network properties explain cognitive flexibility differences between survivors and peers.

The effect of the total small vessel disease burden on the structural brain network

Different cerebral small vessel disease (SVD) lesion types have been shown to disrupt structural brain network individually. Considering that they often coexist, we investigated the relation between their collective effect using the recently proposed total SVD score and structural brain network on MRI in 95 patients with first transient ischemic attack (TIA) or ischemic stroke. Fifty-nine patients with and 36 without any SVD lesions were included. The total SVD score was recorded. Diffusion tensor imaging was performed to estimate structural brain connections for subsequent brain connectivity analysis. The global efficiency and characteristic path length of the structural brain network are respectively lower and higher due to SVD. Lower nodal efficiency is also found in the insular, precuneus, supplementary motor area, paracentral lobule, putamen and hippocampus. The total SVD score is correlated with global network measures, the local clustering coefficient and nodal efficiency of hippocampus, and the nodal efficiency of paracentral lobule. We have successfully demonstrated that the disruption of global and local structural brain networks are associated with the increase in the overall SVD severity or burden of patients with TIA or first-time stroke.

High frequency of silent brain infarcts associated with cognitive deficits in an economically disadvantaged population

OBJECTIVE:

Using magnetic resonance imaging, we aimed to assess the presence of silent brain vascular lesions in a sample of apparently healthy elderly individuals who were recruited from an economically disadvantaged urban region (São Paulo, Brazil). We also wished to investigate whether the findings were associated with worse cognitive performance.

METHODS:

A sample of 250 elderly subjects (66-75 years) without dementia or neuropsychiatric disorders were recruited from predefined census sectors of an economically disadvantaged area of Sao Paulo and received structural magnetic resonance imaging scans and cognitive testing. A high proportion of individuals had very low levels of education (4 years or less, n=185; 21 with no formal education).

RESULTS:

The prevalence of at least one silent vascular-related cortical or subcortical lesion was 22.8% (95% confidence interval, 17.7-28.5), and the basal ganglia was the most frequently affected site (63.14% of cases). The subgroup with brain infarcts presented significantly lower levels of education than the subgroup with no brain lesions as well as significantly worse current performance in cognitive test domains, including memory and attention (p<0.002).

CONCLUSIONS:

Silent brain infarcts were present at a substantially high frequency in our elderly sample from an economically disadvantaged urban region and were significantly more prevalent in subjects with lower levels of education. Covert cerebrovascular disease significantly contributes to cognitive deficits, and in the absence of magnetic resonance imaging data, this cognitive impairment may be considered simply related to ageing. Emphatic attention should be paid to potentially deleterious effects of vascular brain lesions in poorly educated elderly individuals from economically disadvantaged environments.

Advanced Neuroimaging of Cerebral Small Vessel Disease

Cerebral small vessel disease (SVD) is characterised by damage to deep grey and white matter structures of the brain and is responsible for a diverse range of clinical problems that include stroke and dementia. In this review, we describe advances in neuroimaging published since January 2015, mainly with magnetic resonance imaging (MRI), that, in general, are improving quantification, observation and investigation of SVD focussing on three areas: quantifying the total SVD burden, imaging brain microstructural integrity and imaging vascular malfunction. Methods to capture ‘whole brain SVD burden’ across the spectrum of SVD imaging changes will be useful for patient stratification in clinical trials, an approach that we are already testing. More sophisticated imaging measures of SVD microstructural damage are allowing the disease to be studied at earlier stages, will help identify specific factors that are important in development of overt SVD imaging features and in understanding why specific clinical consequences may occur. Imaging vascular function will help establish the precise blood vessel and blood flow alterations at early disease stages and, together with microstructural integrity measures, may provide important surrogate endpoints in clinical trials testing new interventions. Better knowledge of SVD pathophysiology will help identify new treatment targets, improve patient stratification and may in future increase efficiency of clinical trials through smaller sample sizes or shorter follow-up periods. However, most of these methods are not yet sufficiently mature to use with confidence in clinical trials, although rapid advances in the field suggest that reliable quantification of SVD lesion burden, tissue microstructural integrity and vascular dysfunction are imminent.

Biological Relevance of Network Architecture

Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.